WO2024253400A1 - Appareil et système permettant de corriger une erreur de position par analyse et comparaison en temps réel entre des données de vision et des données lidar pour mettre en oeuvre une technologie slam - Google Patents

Appareil et système permettant de corriger une erreur de position par analyse et comparaison en temps réel entre des données de vision et des données lidar pour mettre en oeuvre une technologie slam Download PDF

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WO2024253400A1
WO2024253400A1 PCT/KR2024/007583 KR2024007583W WO2024253400A1 WO 2024253400 A1 WO2024253400 A1 WO 2024253400A1 KR 2024007583 W KR2024007583 W KR 2024007583W WO 2024253400 A1 WO2024253400 A1 WO 2024253400A1
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data
module
vision
location information
location
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English (en)
Korean (ko)
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이종민
옥지성
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Integrit Inc
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Integrit Inc
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Priority claimed from KR1020230073292A external-priority patent/KR102631313B1/ko
Priority claimed from KR1020230073293A external-priority patent/KR102631315B1/ko
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G01S17/894—Three-dimensional [3D] imaging with simultaneous measurement of time-of-flight at a two-dimensional [2D] array of receiver pixels, e.g. time-of-flight cameras or flash lidar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/24—Arrangements for determining position or orientation
    • G05D1/243—Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/24—Arrangements for determining position or orientation
    • G05D1/246—Arrangements for determining position or orientation using environment maps, e.g. simultaneous localisation and mapping [SLAM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces

Definitions

  • An embodiment of the present invention relates to a device and system capable of correcting position errors through real-time analysis and comparison between vision data and lidar data for implementing SLAM technology.
  • Autonomous driving refers to driving to a destination by making decisions on your own without the intervention of a robot or user. To do this, you need a 2D or 3D map of the environment you want to drive in. Since a 2D map is made on a flat surface, obstacles that are not recognized by the 2D sensor are not mapped.
  • SLAM Simultaneous Localization and Mapping
  • vSLAM Vision-Based SLAM
  • image sensor camera
  • the camera can be largely divided into three types: 'Monocular', 'Stereo', and 'RGB-D'.
  • the 'Monocular Camera' uses only one camera sensor. Therefore, it has the advantage of being very simple in structure and cheap, but since it loses depth or distance information, 'Monocular SLAM' can predict 'motion', 'distance', 'size' of 'object', etc. through continuous images according to the movement of the camera.
  • vSLAM's framework can be composed of 'Sensor Data Acquisition', 'Visual Odometry (VO)', 'Backend Filtering/Optimization', 'Loop Closing', and 'Reconstruction'.
  • 'Sensor Data Acquisition' is the process of obtaining and preprocessing camera images.
  • 'Motor encoder' and 'IMU sensor values' can be obtained from a moving robot, and the 'synchronization' process can also be performed.
  • VO Vehicle Odometry
  • 'Backend Filtering/Optimization' can receive camera poses and 'loop closing' results at different time steps and apply an optimization algorithm to create an optimized trajectory and map.
  • the 'Loop Closing' can provide information (whether the robot has returned to the previous position) to 'optimization' in the 'backend' by determining whether the robot has returned to the previous position to reduce the accumulated drift.
  • the drift problem refers to the problem that occurs when errors continuously occur in 'Visual Odometry' and accumulate.
  • 'Reconstruction' can construct a map according to the robot's task based on the estimated camera trajectory.
  • 'Visual Odometry' estimates the camera movement between adjacent image frames and connects them based on time steps. Since the error occurring at each time step is applied to the next 'Visual Odometry', errors continue to accumulate, which is called the 'accumulated drift problem'.
  • 'Loop Closing' is a method to solve the 'accumulated drift problem'.
  • the estimated result may not return to the origin due to the error at that time. If there is a way to tell that the robot has returned to the origin, the error can be removed by pulling the estimated position information back to the origin.
  • One way to make the robot recognize the origin is to place a marker at the starting point, and when the robot recognizes the marker, it can be known that the robot has returned to the starting point.
  • Patent Publication No. 10-2022-0152451 Publication date: November 16, 2022.
  • An embodiment of the present invention provides a SLAM position error correction device and a SLAM position error correction system that are linked with a cloud device, which configures a database by matching images and coordinate information based on visual information, performs a similarity test between images acquired in real time and the database through visual loop detection, estimates position information using visual information, and then readjusts a position estimated by LiDAR and odometry using the estimated information.
  • a device capable of correcting position errors through real-time analysis and comparison between vision data and LiDAR data for implementing SLAM (Simultaneous Localization And Map-Building) technology includes: a DB module that matches and stores a plurality of vision data and a plurality of standard location information generated in advance, and stores them as a plurality of location groups each grouped according to a set area having a certain coordinate range; a sensor module that calculates estimated location information of an autonomous driving robot through LiDAR and odometry and specifies one of the plurality of location groups according to the calculated estimated location information; a vision module that provides a vision recognition result through a vision recognition process for a driving image of the autonomous driving robot; a location information extraction module that extracts standard location information that matches vision data most similar to the vision recognition result among vision data belonging to the location group specified through the sensor module; And it includes a location information reset module that compares the standard location information extracted through the location information extraction module and the estimated location information calculated through the sensor
  • a system capable of correcting position errors through real-time analysis and comparison between vision data and lidar data for implementing SLAM (Simultaneous Localization And Map-Building) technology comprises: an autonomous driving robot including a sensor module and a vision module; And a DB module, a location information extraction module, and a location information reset module are included, and a cloud server connected to the autonomous driving robot through an Internet communication network, wherein the DB module matches a plurality of vision data and a plurality of standard location information generated in advance and stores them, and stores them as a plurality of location groups each grouped according to a set area having a certain coordinate range, the sensor module calculates estimated location information of the autonomous driving robot through LiDAR and Odometry, and specifies one location group among the plurality of location groups according to the calculated estimated location information, the vision module provides a vision recognition result through a vision recognition process for a driving image of the autonomous driving robot, the location information extraction module extracts standard location information that matches vision data most similar to the
  • a system capable of correcting position errors through real-time analysis and comparison between vision data and LiDAR data for implementing SLAM (Simultaneous Localization And Map-Building) technology comprises: a DB module that matches and stores a plurality of vision data and a plurality of standard location information generated in advance, and stores them as a plurality of location groups each grouped according to a set area having a certain coordinate range; a sensor module that calculates estimated location information of an autonomous driving robot through LiDAR and odometry and specifies one of the plurality of location groups according to the calculated estimated location information; a vision module that provides a vision recognition result through a vision recognition process for a driving image of the autonomous driving robot; a location information extraction module that extracts standard location information that matches vision data that is most similar to the vision recognition result among vision data belonging to the location group specified through the sensor module; An autonomous driving robot including a location information reset module that compares the standard location information extracted through the location information extraction module with the estimated location information
  • a system capable of correcting position errors through real-time analysis and comparison between vision data and LiDAR data for implementing SLAM (Simultaneous Localization And Map-Building) technology comprises: a first DB module which matches and stores a plurality of vision data and a plurality of standard location information generated in advance, and stores them as a plurality of location groups each grouped according to a set area having a certain coordinate range; a sensor module which calculates estimated location information of an autonomous driving robot through LiDAR and odometry and specifies one of the plurality of location groups according to the calculated estimated location information; a vision module which provides a vision recognition result through a vision recognition process for a driving image of the autonomous driving robot; a first location information extraction module which extracts standard location information which matches vision data most similar to the vision recognition result among vision data belonging to the location group specified through the sensor module; And an autonomous driving robot including a location information reset module that compares the standard location information extracted through the first location information extraction module and the estimated location information
  • a SLAM position error correction device and a SLAM position error correction system that are linked with a cloud device can be provided, which configures a database by matching images and coordinate information based on visual information, performs a similarity test between images acquired in real time and the database through visual loop detection, estimates position information using visual information, and then readjusts a position estimated by LiDAR and odometry using the estimated information.
  • FIG. 1 is a drawing showing the overall configuration and configuration form of a SLAM position error correction device applied to an autonomous driving robot according to the first embodiment of the present invention.
  • FIG. 2 is a block diagram showing the overall configuration and configuration relationship of a SLAM position error correction device according to the first embodiment of the present invention.
  • FIG. 3 is a drawing illustrating the overall operation process of a SLAM position error correction device according to the first embodiment of the present invention.
  • Figure 4 is a block diagram showing the configuration of a DB module according to the first embodiment of the present invention.
  • FIG. 5 is a graph showing an example of selecting a location group according to the first embodiment of the present invention.
  • Figure 6 is a block diagram showing the configuration of a vision module according to the first embodiment of the present invention.
  • Figure 7 is a block diagram showing the configuration of a location information extraction module according to the first embodiment of the present invention.
  • FIG. 8 is a drawing illustrating an operation process according to position readjustment of a position information reset module according to the first embodiment of the present invention.
  • FIG. 9 is a diagram illustrating the overall operation process of the DB update module according to the first embodiment of the present invention.
  • FIG. 10 is a drawing showing the overall configuration and configuration form of a SLAM position error correction system according to the second embodiment of the present invention.
  • Figure 11 is a block diagram showing the configuration of a vision module according to the second embodiment of the present invention.
  • Figure 12 is a block diagram showing the configuration of a DB module according to the second embodiment of the present invention.
  • Figure 13 is a block diagram showing the configuration of a location information extraction module according to the second embodiment of the present invention.
  • FIG. 14 is a drawing showing the overall configuration and configuration form of a SLAM position error correction system according to the third embodiment of the present invention.
  • Figure 15 is a block diagram showing the configuration of a DB module according to the third embodiment of the present invention.
  • Figure 16 is a block diagram showing the configuration of a vision module according to a third embodiment of the present invention.
  • Figure 17 is a block diagram showing the configuration of a location information extraction module according to the third embodiment of the present invention.
  • FIG. 18 is a drawing showing the overall configuration and configuration form of a SLAM position error correction system according to the fourth embodiment of the present invention.
  • Figure 19 is a block diagram showing the configuration of a DB module according to the fourth embodiment of the present invention.
  • Figure 20 is a block diagram showing the configuration of a vision module according to the fourth embodiment of the present invention.
  • Figure 21 is a block diagram showing the configuration of a location information extraction module according to the fourth embodiment of the present invention.
  • FIG. 1 is a diagram showing the overall configuration and configuration form of a SLAM position error correction device applied to an autonomous driving robot according to the first embodiment of the present invention
  • FIG. 2 is a block diagram showing the overall configuration and configuration relationship of a SLAM position error correction device according to the first embodiment of the present invention
  • FIG. 3 is a diagram illustrating the overall operation process of the SLAM position error correction device according to the first embodiment of the present invention
  • FIG. 4 is a block diagram showing the configuration of a DB module according to the first embodiment of the present invention
  • FIG. 5 is a graph showing an example of selecting a position group according to the first embodiment of the present invention
  • FIG. 6 is a block diagram showing the configuration of a vision module according to the first embodiment of the present invention
  • FIG. 1 is a diagram showing the overall configuration and configuration form of a SLAM position error correction device applied to an autonomous driving robot according to the first embodiment of the present invention
  • FIG. 2 is a block diagram showing the overall configuration and configuration relationship of a SLAM
  • FIG. 7 is a block diagram showing the configuration of a position information extraction module according to the first embodiment of the present invention
  • FIG. 8 is a diagram illustrating the operation process according to position readjustment of a position information reset module according to the first embodiment of the present invention
  • FIG. 9 is a diagram illustrating the overall operation process of a DB update module according to the first embodiment of the present invention.
  • a SLAM position error correction device (1000) applied to an autonomous driving robot (10) may include at least one of a DB module (1100), a sensor module (1200), a vision module (1300), a position information extraction module (1400), a position information reset module (1500), and a DB update module (1600).
  • the above DB module (1100) can store a plurality of pre-generated vision data and a plurality of standard location information by matching them with each other, and can store them as a plurality of location groups each grouped according to a set area having a certain coordinate range.
  • Such DB module (1100) may include at least one of a location group creation unit (1110) and a location group management unit (1120), as illustrated in FIG. 4.
  • the above location group generation unit (1110) matches the DB vector data extracted from each vision data (image of a driving video) and the standard location information representing the coordinate information where each vision data is generated to generate each data set, and groups the generated multiple data sets according to a set area having a certain coordinate range to generate multiple location groups.
  • the set area means a range for selecting a location group for performing a similarity search at the location of the autonomous driving robot (10), and standard location information that is divided and fixed at a certain interval is defined in one location group.
  • a similarity comparison search can be performed only in that group, and in the case of 'S2', since the location where the autonomous driving robot (10) is displayed is located at the boundary of the group, it means that the surrounding groups must also be searched, and accordingly, the group where a similarity comparison search is to be performed can be selected relatively more widely, such as 'S2'.
  • a number of standard location information i.e., standard coordinate data
  • an image matching each standard coordinate is defined as vector data.
  • the above location group management unit (1120) can store a data set of a location group created through the location group creation unit (1110), and when a data update command and data to be updated are input by the DB update module (1600), it can modify previously stored data.
  • the above sensor module (1200) calculates estimated location information (information estimating the location of the robot itself) of the autonomous driving robot (10) through LiDAR and Odometry systems, and can specify one location group among a number of location groups defined in the DB module (1100) based on the calculated estimated location information.
  • LiDAR can detect and measure the distance to a target object by emitting a 360-degree laser pulse and calculating the time it takes for the light to reflect off surrounding objects such as walls or obstacles and return to recognize the surrounding environment. Odometry measures the rotational speed through an encoder and the inclination, etc., using an IMU sensor to measure the position of a moving object, thereby finding its own position on the map, that is, the position of the autonomous driving robot (10) (estimated position information), and driving.
  • the above sensor module (1200) can compare the estimated location information of the autonomous driving robot (10) calculated through LiDAR and Odometry with the set area defined for each location group to determine the location group corresponding to the estimated location information of the autonomous driving robot (10).
  • the sensor module (1200) can check which location group the estimated location information of the autonomous driving robot (10) belongs to and output the estimated location information and the location group ID or identification information corresponding to the estimated location information.
  • the above vision module (1300) can provide a vision recognition result through a vision recognition process for a driving image of an autonomous driving robot (10).
  • the vision module (1300) may include at least one of a driving image generation unit (1310), a driving image acquisition unit (1320), and a vector data extraction unit (1330), as illustrated in FIG. 6.
  • the above driving image generation unit (1310) can generate driving images of the autonomous driving robot (10) through at least two cameras installed in the autonomous driving robot (10) and shooting from different directions or angles.
  • driving images of an autonomous driving robot can be generated using a camera facing the ceiling and a camera facing the front, respectively.
  • the above driving image acquisition unit (1320) can acquire images of driving images of the autonomous driving robot (10) at regular intervals and assign an image ID to each acquired image.
  • the above vector data extraction unit (1330) can extract current vector data from each driving image acquired through the driving image acquisition unit (1320) and provide it as a vision recognition result.
  • the 'current vector data' and 'DB vector data' mentioned below are both composed of lines created by connecting points and are data that can be expressed as a mathematically calculated functional relationship, and are terms of the same concept.
  • the vector data generated in the vision module (1300) is defined as 'current vector data'
  • the vector data stored in the DB module (1100) is defined as 'DB vector data' to distinguish similar terms.
  • the 'estimated location information' described above and the 'standard location information' mentioned below are the same in that they mean coordinate information indicating the location of the autonomous driving robot (10), but the 'estimated location information' is coordinate information calculated through the sensor module (1200), and the 'standard location information' can be distinguished as coordinate information that is matched with 'DB vector data' in the DB module (1100) and stored in advance.
  • the above location information extraction module (1400) can find the vision data (DB vector data) most similar to the vision recognition result (current vector data) among the vision data (DB vector data) belonging to a specific location group through the sensor module (1200), and extract standard location information matching the corresponding vision data (DB vector data).
  • the location information extraction module (1400) may include at least one of a data input unit (1410), a location group selection unit (1420), a similarity result calculation unit (1430), and a location information provision unit (1440), as illustrated in FIG. 7.
  • the above data input unit (1410) can receive current vector data from the vision module (1300) and input estimated location information and specific location group information (location group ID or identification information) from the sensor module (1200), respectively. Accordingly, information on the image currently being captured through the vision module (1300), location information recognized through the sensor module (1200), and information on the location group corresponding to the location information can be input, respectively.
  • the above location group selection unit (1420) can select a location group stored in the DB module (1100) according to the location group information (location group ID or identification information) input through the data input unit (1410). In this way, instead of performing a comparison process on the entire DB image to find an image similar to the image recognized through the vision module (1300), by specifying a group belonging to the location recognized through the sensor module (1200), candidate images to be compared with the image recognized by the vision module (1300) can be specified, thereby reducing the number of comparison targets. Accordingly, since the number of comparisons is reduced, the time required for the similarity comparison process can be reduced, and errors such as cases where images are similar but located in different places can be eliminated, thereby increasing accuracy.
  • the above similarity result generating unit (1430) can perform a cosine similarity measurement between 'DB vector data' belonging to a location group selected through a location group selecting unit (1420) and 'current vector data' input through a data input unit (1410) and calculate a score accordingly.
  • the above location information providing unit (1440) checks whether the highest similarity score among the similarity scores calculated through the similarity result calculating unit (1430) exceeds a preset threshold score, and if the highest similarity score exceeds the threshold score, extracts and provides standard location information that matches the DB vector data that received the highest similarity score.
  • DB vector data '2' is compared with a threshold score, and if it is confirmed that the similarity score of DB vector data '2' exceeds the threshold score, the standard location information '2' matching DB vector data '2' can be extracted.
  • the above location information reset module (1500) can compare the standard location information extracted through the location information extraction module (1400) and the estimated location information calculated through the sensor module (1200), and reset the location information of the autonomous driving robot (10) based on the comparison result.
  • the location information reset module (1500) compares the estimated location information produced by the sensor module (1200) with the standard location information provided by the location information provider (1440) to determine whether they match, and if they do not match, as shown in FIG. 8, it determines that there is an abnormality in the driving state of the autonomous driving robot (10), resets the LiDAR and Odometry of the sensor module (1200) while the autonomous driving robot (10) is temporarily stopped, and then sets the standard location information provided by the location information provider (1440) as the initial location information of the LiDAR and Odometry to correct the location error.
  • This location information reset module (1500) performs a similarity check between an image acquired in real time through visual loop detection and a pre-built database, and can readjust the initial location information from LiDAR and Odometry based on the location information estimated using visual information when there is an abnormality in the driving status of the autonomous driving robot (10).
  • position readjustment refers to a process of resetting the current position information of the autonomous driving robot (10) by resetting the odometry and assigning initial position information after temporarily stopping the autonomous driving robot (10) when it is determined that the driving state of the autonomous driving robot (10) is incorrect, as shown in FIG. 8.
  • an algorithm for estimating the current position information is used to reset the position information estimated using visual information to the initial position information when an abnormality in the driving state of the autonomous driving robot (10) is detected, thereby correcting the error in the current position information.
  • the above DB update module (1600) adds the image of the current vector data having the highest similarity score to the candidate DB if the highest similarity score does not exceed a threshold, and then performs a similarity comparison with vector data previously added to the candidate DB. If the image exceeds the threshold, it performs an up-count on the image ID of the corresponding vector data. If the image does not exceed the threshold, it assigns a new image ID and adds it to the candidate DB.
  • the DB module (1100) can update the data set of the DB module (1100) by additionally registering the average value of the current vector data having the highest similarity score and the estimated location information corresponding to the current vector data.
  • the image of the current vector data with the highest similarity score is added to the candidate DB, and the vector data added to the candidate DB is compared for similarity with the vector data existing in the existing candidate DB. If there is a similar vector (i.e., if it exceeds the reference score), an upcount is performed on the image ID of the corresponding vector data and estimated location information is added. If there is no similar vector data, a new ID is assigned to the image of the corresponding vector data and stored in the candidate DB.
  • the average value of the corresponding data and the estimated location information corresponding to the corresponding data is additionally registered in the DB module (1100), thereby updating the data set of the DB module (1100), and the corresponding data can be removed from the candidate DB.
  • the inside of the building may change over time, so the image (i.e., vector data) stored in the DB module (1100) must also be updated accordingly. Accordingly, when the DB update module (1600) compares the image provided through the vision module (1300) with the image stored in the DB module (1100), if a similar image is not found (i.e., if the highest similarity score does not exceed the reference score), vector data and coordinate information for the corresponding image can be additionally registered in the DB module (1100).
  • the additional registration target through a single judgment process may rather contaminate the DB, if the above condition (the number of accumulated counters is greater than the preset standard number of times) is satisfied N or more times (e.g. 3 or more times), the corresponding vector data (current N pieces of vector data) can be added to the DB module (1100), and at this time, the average value of the N pieces of estimated location information provided by the sensor module (1200) when the current vector data is acquired from the vision module (1300) can be applied as the location information.
  • N or more times e.g. 3 or more times
  • FIG. 10 is a drawing showing the overall configuration and configuration form of a SLAM position error correction system according to the second embodiment of the present invention
  • FIG. 11 is a block diagram showing the configuration of a vision module according to the second embodiment of the present invention
  • FIG. 12 is a block diagram showing the configuration of a DB module according to the second embodiment of the present invention
  • FIG. 13 is a block diagram showing the configuration of a position information extraction module according to the second embodiment of the present invention.
  • the SLAM position error correction system (2000) may include at least one of an autonomous driving robot (2100) and a cloud server (2200).
  • the above autonomous driving robot (2100) may include at least one of a sensor module (2110) and a vision module (2120), and the cloud server (2200) may include at least one of a DB module (2210), a location information extraction module (2220), a location information reset module (2230), and a DB update module (2240).
  • the above autonomous driving robot (2100) is connected to a cloud server (2200) through an Internet communication network and can perform an operation for position error correction by interworking with the cloud server (2200).
  • a sensor module (2110) and a vision module (2120) configured in an autonomous driving robot (2100), and a DB module (2210), a location information extraction module (2220), a location information reset module (2230), and a DB update module (2240) configured in a cloud server (2200).
  • the above sensor module (2110) calculates estimated location information (information estimating the location of the robot itself) of the autonomous driving robot (2100) through LiDAR and Odometry systems, and can specify one location group among a number of location groups defined in the DB module (2210) based on the calculated estimated location information.
  • the above sensor module (2110) can compare the estimated location information of the autonomous driving robot (2100) calculated through LiDAR and Odometry with the set area defined for each location group to determine the location group corresponding to the estimated location information of the autonomous driving robot (2100).
  • the sensor module (2110) can check which location group the estimated location information of the autonomous driving robot (2100) belongs to and output the estimated location information and the location group ID or identification information corresponding to the estimated location information.
  • the above vision module (2120) can provide a vision recognition result through a vision recognition process for a driving image of an autonomous driving robot (2100).
  • the vision module (2120) may include at least one of a driving image generation unit (2121), a driving image acquisition unit (2122), and a vector data extraction unit (2123), as illustrated in FIG. 11.
  • the above driving image generation unit (2121) can generate driving images of the autonomous driving robot (2100) through at least two cameras installed in the autonomous driving robot (2100) and shooting images in different directions or angles.
  • driving images of an autonomous driving robot (2100) can be generated using a camera facing the ceiling and a camera facing the front, respectively.
  • the above driving image acquisition unit (2122) can acquire images of driving images of the autonomous driving robot (2100) at regular intervals and assign an image ID to each acquired image.
  • the above vector data extraction unit (2123) can extract current vector data from each driving image acquired through the driving image acquisition unit (2122) and provide it as a vision recognition result.
  • the above DB module (2210) can store a plurality of pre-generated vision data and a plurality of standard location information by matching them with each other, and can store them as a plurality of location groups each grouped according to a set area having a certain coordinate range.
  • Such DB module (2210) may include at least one of a location group creation unit (2211) and a location group management unit (2212), as illustrated in FIG. 12.
  • the above location group generation unit (2211) matches the DB vector data extracted from each vision data (image of a driving video) and the standard location information representing the coordinate information where each vision data is generated to generate each data set, and groups the generated multiple data sets according to a set area having a certain coordinate range to generate multiple location groups.
  • the set area means a range for selecting a location group for performing a similarity search at the location of the autonomous driving robot (10), and standard location information that is divided and fixed at a certain interval is defined in one location group.
  • a similarity comparison search can be performed only in that group, and in the case of 'S2', since the location where the autonomous driving robot (10) is displayed is located at the boundary of the group, it means that the surrounding groups must also be searched, and accordingly, the group where a similarity comparison search is to be performed can be selected relatively more widely, such as 'S2'.
  • the above location group management unit (2212) can store a data set of a location group created through a location group creation unit (2211), and when a data update command and data to be updated are input by a DB update module (2240), it can modify previously stored data.
  • the above location information extraction module (2220) can find the vision data (DB vector data) most similar to the vision recognition result (current vector data) among the vision data (DB vector data) belonging to a specified location group through the sensor module (2110) and extract standard location information matching the vision data (DB vector data).
  • the location information extraction module (2220) may include at least one of a data input unit (2221), a location group selection unit (2222), a similarity result calculation unit (2123), and a location information provision unit (2224), as illustrated in FIG. 13.
  • the above data input unit (2221) can receive current vector data from the vision module (2120) and input estimated location information and specific location group information (location group ID or identification information) from the sensor module (2110), respectively. Accordingly, information on the image currently being captured through the vision module (2120), location information recognized through the sensor module (2110), and information on the location group corresponding to the location information can be input, respectively.
  • the above location group selection unit (2222) can select a location group stored in the DB module (2210) based on the location group information (location group ID or identification information) input through the data input unit (2221).
  • the above similarity result calculation unit (2123) can perform a cosine similarity measurement between the 'DB vector data' belonging to the location group selected through the location group selection unit (2222) and the 'current vector data' input through the data input unit (2221) and calculate a score accordingly.
  • the above location information providing unit (2224) checks whether the highest similarity score among the similarity scores calculated through the similarity result calculating unit (2123) exceeds a preset threshold score, and if the highest similarity score exceeds the threshold score, extracts and provides standard location information that matches the DB vector data that received the highest similarity score.
  • the above location information reset module (2230) can compare the standard location information extracted through the location information extraction module (2220) and the estimated location information calculated through the sensor module (2110), and reset the location information of the autonomous driving robot (10) based on the comparison result.
  • the location information reset module (2230) compares the estimated location information produced by the sensor module (2110) with the standard location information provided by the location information provider (2224) to determine whether they match, and if they do not match, determines that there is an abnormality in the driving status of the autonomous driving robot (10), resets the LiDAR and Odometry of the sensor module (2110) while the autonomous driving robot (10) is temporarily stopped, and then sets the standard location information provided by the location information provider (122s) as the initial location information of the LiDAR and Odometry to correct the location error.
  • This location information reset module (2230) performs a similarity check between an image acquired in real time through visual loop detection and a pre-built database, and can readjust the initial location information from LiDAR and Odometry based on the location information estimated using visual information when there is an abnormality in the driving status of the autonomous driving robot (10).
  • the above DB update module (2240) adds the image of the current vector data having the highest similarity score to the candidate DB if the highest similarity score does not exceed a threshold, and then performs a similarity comparison with vector data previously added to the candidate DB. If the similarity score exceeds the threshold, it performs an up-count on the image ID of the corresponding vector data. If the similarity score does not exceed the threshold, it assigns a new image ID and adds it to the candidate DB.
  • the DB module (2210) can additionally register the average value of the current vector data having the highest similarity score and the estimated location information corresponding to the current vector data to the DB module (2210) to update the data set of the DB module (2210).
  • FIG. 14 is a drawing showing the overall configuration and configuration form of a SLAM position error correction system according to the third embodiment of the present invention
  • FIG. 15 is a block diagram showing the configuration of a DB module according to the third embodiment of the present invention
  • FIG. 16 is a block diagram showing the configuration of a vision module according to the third embodiment of the present invention
  • FIG. 17 is a block diagram showing the configuration of a position information extraction module according to the third embodiment of the present invention.
  • the SLAM position error correction system (3000) may include at least one of an autonomous driving robot (3100) and a cloud server (3200).
  • the above autonomous driving robot (3100) may include at least one of a DB module (3110), a sensor module (3120), a vision module (3130), a location information extraction module (3140), a location information reset module (3150), a DB update module (3160), and a DB monitoring module (3170), and the cloud server (2200) may include at least one of a DB backup module (3210) and a DB modification module (3220).
  • the above autonomous driving robot (3100) can operate in a manner that is connected to a cloud server (3200) through an Internet communication network and interoperates with the cloud server (3200).
  • the above DB module (3110) can store a plurality of pre-generated vision data and a plurality of standard location information by matching them with each other, and can store them as a plurality of location groups each grouped according to a set area having a certain coordinate range.
  • Such DB module (3110) may include at least one of a location group creation unit (3111) and a location group management unit (3112), as illustrated in FIG. 15.
  • the above location group generation unit (3111) can generate data sets by matching DB vector data extracted from each vision data (image of a driving video) and standard location information representing coordinate information where each vision data is generated, and can group multiple generated data sets according to a set area having a certain coordinate range to generate multiple location groups.
  • each location group can be defined for each setting area 'S1' (or setting area 'S2' wider than 'S1') consisting of a certain coordinate range.
  • a plurality of standard location information (location) that is, standard coordinate data, are defined at certain distance intervals, and an image matching each standard coordinate is defined as vector data.
  • the above location group management unit (3112) can store a data set of a location group created through a location group creation unit (3111), and when a data update command and data to be updated are input by a DB update module (3160), it can modify previously stored data.
  • the above sensor module (3120) calculates estimated location information (information estimating the location of the robot itself) of the autonomous driving robot (3100) through LiDAR and Odometry systems, and can specify one location group among a number of location groups defined in the DB module (3110) based on the calculated estimated location information.
  • the above sensor module (3120) can compare the estimated location information of the autonomous driving robot (3100) calculated through LiDAR and Odometry with the set area defined for each location group to determine the location group corresponding to the estimated location information of the autonomous driving robot (3100).
  • the sensor module (3120) can check which location group the estimated location information of the autonomous driving robot (3100) belongs to and output the estimated location information and the location group ID or identification information corresponding to the estimated location information.
  • the above vision module (3130) can provide a vision recognition result through a vision recognition process for a driving image of an autonomous driving robot (3100).
  • the vision module (3130) may include at least one of a driving image generation unit (3131), a driving image acquisition unit (3132), and a vector data extraction unit (3133), as illustrated in FIG. 16.
  • the above driving image generation unit (3131) can generate driving images of the autonomous driving robot (3100) through at least two cameras installed in the autonomous driving robot (3100) and shooting images in different directions or angles.
  • driving images of an autonomous driving robot (3100) can be generated using a camera facing the ceiling and a camera facing the front, respectively.
  • the above driving image acquisition unit (3132) can acquire images of driving images of the autonomous driving robot (3100) at regular intervals and assign an image ID to each acquired image.
  • the above vector data extraction unit (3133) can extract current vector data from each driving image acquired through the driving image acquisition unit (3132) and provide it as a vision recognition result.
  • the above location information extraction module (3140) can find the vision data (DB vector data) most similar to the vision recognition result (current vector data) among the vision data (DB vector data) belonging to a specific location group through the sensor module (3120), and extract standard location information matching the vision data (DB vector data).
  • the location information extraction module (3140) may include at least one of a data input unit (3141), a location group selection unit (3142), a similarity result calculation unit (3143), and a location information provision unit (3144), as illustrated in FIG. 17.
  • the above data input unit (3141) can receive current vector data from the vision module (3130) and input estimated location information and specific location group information (location group ID or identification information) from the sensor module (3120), respectively. Accordingly, information on the image currently being captured through the vision module (3130), location information recognized through the sensor module (3120), and information on the location group corresponding to the location information can be input, respectively.
  • the above location group selection unit (3142) can select a location group stored in the DB module (3110) based on location group information (location group ID or identification information) input through the data input unit (3141).
  • the above similarity result producing unit (3143) can perform a cosine similarity measurement between 'DB vector data' belonging to a location group selected through a location group selecting unit (3142) and 'current vector data' input through a data input unit (3141) and produce a score accordingly.
  • the above location information providing unit (3144) checks whether the highest similarity score among the similarity scores calculated through the similarity result calculating unit (3143) exceeds a preset threshold score, and if the highest similarity score exceeds the threshold score, extracts and provides standard location information that matches the DB vector data that received the highest similarity score.
  • the above location information reset module (3150) can compare the standard location information extracted through the location information extraction module (3140) and the estimated location information calculated through the sensor module (3120), and reset the location information of the autonomous driving robot (10) based on the comparison result.
  • the location information reset module (3150) compares the estimated location information produced by the sensor module (3120) with the standard location information provided by the location information provider (3144) to determine whether they match, and if they do not match, determines that there is an abnormality in the driving status of the autonomous driving robot (3100), resets the LiDAR and Odometry of the sensor module (3120) while the autonomous driving robot (3100) is temporarily stopped, and then sets the standard location information provided by the location information provider (3144) as the initial location information of the LiDAR and Odometry to correct the location error.
  • This location information reset module (3150) performs a similarity check between an image acquired in real time through visual loop detection and a pre-built database, and can readjust the initial location information from LiDAR and Odometry based on the location information estimated using visual information when there is an abnormality in the driving status of the autonomous driving robot (3100).
  • the above DB update module (3160) adds the image of the current vector data having the highest similarity score to the candidate DB if the highest similarity score does not exceed a threshold, and then performs a similarity comparison with vector data previously added to the candidate DB. If the similarity score exceeds the threshold, it performs an up-count on the image ID of the corresponding vector data, and if the similarity score does not exceed the threshold, it assigns a new image ID and adds it to the candidate DB.
  • the DB module (3110) can additionally register the average value of the current vector data having the highest similarity score and the estimated location information corresponding to the current vector data to the DB module (3110) to update the data set of the DB module (3110).
  • the above DB monitoring module (3170) can perform monitoring on data (data set including DB vector data and standard location information) stored in the DB module (3110) and generate DB monitoring data.
  • the DB monitoring module (3170) can monitor the initial creation time information of vision data, the most recent time information selected by the highest similarity score for each vision data, the number of times information selected by the highest similarity score for each vision data, and the comparison information on the distance and direction difference between the standard location information selected by the highest similarity score and the estimated location information of the sensor module (3120).
  • the above DB backup module (3210) can be synchronized with the DB module (3110) of the autonomous driving robot (3100) and perform the role of backing up and managing data (data set including DB vector data and standard location information) stored in the DB module (3110).
  • the above DB modification module (3220) can perform the role of determining data that is not necessary for the DB module (3110) based on DB monitoring data provided from the DB monitoring module (3170) of the autonomous driving robot (3100) and deleting and managing the corresponding data.
  • the final data (data set including DB vector data and standard location information) stored in the autonomous driving robot (3100) are stored in the DB backup module (3210) of the cloud server (3200) at regular intervals, and by performing a modification task through an administrator on the data stored in the DB backup module (3210) and then transmitting it to the autonomous driving robot (3100), the modified data can be applied to the autonomous driving robot (3100).
  • Modification of data in the DB module (3110) can increase the reliability of data stored in the DB module (3110) by removing data that is no longer needed and data with errors according to the changing surrounding environment in addition to the DB automatic update algorithm that is performed locally, i.e., in the autonomous driving robot (3100).
  • the administrator can modify the data in the DB module (3110) by selecting data to be removed or removing data that satisfies specific conditions through an algorithm, thereby applying it to the autonomous driving robot (3100).
  • FIG. 18 is a drawing showing the overall configuration and configuration form of a SLAM position error correction system according to the fourth embodiment of the present invention
  • FIG. 19 is a block diagram showing the configuration of a first DB module according to the fourth embodiment of the present invention
  • FIG. 20 is a block diagram showing the configuration of a vision module according to the fourth embodiment of the present invention
  • FIG. 31 is a block diagram showing the configuration of a first position information extraction module according to the fourth embodiment of the present invention.
  • the SLAM position error correction system (4000) may include at least one of an autonomous driving robot (4100) and a cloud server (4200).
  • the above autonomous driving robot (4100) may include at least one of a first DB module (4110), a sensor module (4120), a vision module (4130), a first location information extraction module (4140), a location information reset module (4150), and a DB update module (4160), and the cloud server (2200) may include at least one of a second first DB module (4210) and a second location information extraction module (4220).
  • the above autonomous driving robot (4100) can operate in a manner that is connected to a cloud server (4200) through an Internet communication network and interoperates with the cloud server (4200).
  • the above first DB module (4110) can store a plurality of pre-generated vision data and a plurality of standard location information by matching them with each other, and can store them as a plurality of location groups each grouped according to a set area having a certain coordinate range.
  • This first DB module (4110) may include at least one of a location group creation unit (4111) and a location group management unit (4112), as illustrated in FIG. 19.
  • the above location group generation unit (4111) matches the DB vector data extracted from each vision data (image of a driving video) and the standard location information representing the coordinate information where each vision data is generated to generate each data set, and groups the generated multiple data sets according to a set area having a certain coordinate range to generate multiple location groups.
  • the set area means a range for selecting a location group for performing a similarity search at the location of the autonomous driving robot (4100), and standard location information that is divided and fixed at a certain interval is defined in one location group.
  • a similarity comparison search only needs to be performed in that group, and in the case of 'S2', since the location where the autonomous driving robot (4100) is displayed is located at the boundary of the group, it means that the surrounding groups need to be searched as well, and accordingly, a group where a similarity comparison search is to be performed can be selected relatively more widely, such as 'S2'.
  • the above location group management unit (4112) can store a data set of a location group created through a location group creation unit (4111), and when a data update command and data to be updated are input by a DB update module (4160), it can modify previously stored data.
  • the above sensor module (4120) calculates estimated location information (information estimating the location of the robot itself) of the autonomous driving robot (4100) through a LiDAR and Odometry system, and can specify one location group among a plurality of location groups defined in the first DB module (4110) based on the calculated estimated location information.
  • the above sensor module (4120) can compare the estimated location information of the autonomous driving robot (4100) calculated through LiDAR and Odometry with the set area defined for each location group to determine the location group corresponding to the estimated location information of the autonomous driving robot (4100).
  • the sensor module (4120) can check which location group the estimated location information of the autonomous driving robot (4100) belongs to and output the estimated location information and the location group ID or identification information corresponding to the estimated location information.
  • the above vision module (4130) can provide a vision recognition result through a vision recognition process for a driving image of an autonomous driving robot (4100).
  • the vision module (4130) may include at least one of a driving image generation unit (4131), a driving image acquisition unit (4132), and a vector data extraction unit (4133), as illustrated in FIG. 20.
  • the above driving image generation unit (4131) can generate driving images of the autonomous driving robot (4100) through at least two cameras installed in the autonomous driving robot (4100) and shooting images in different directions or angles.
  • driving images of an autonomous driving robot (4100) can be generated using a camera facing the ceiling and a camera facing the front, respectively.
  • the above driving image acquisition unit (4132) can acquire images of driving images of the autonomous driving robot (4100) at regular intervals and assign an image ID to each acquired image.
  • the above vector data extraction unit (4133) can extract current vector data from each driving image acquired through the driving image acquisition unit (4132) and provide it as a vision recognition result.
  • the above first location information extraction module (4140) can find the vision data (DB vector data) most similar to the vision recognition result (current vector data) among the vision data (DB vector data) belonging to a specified location group through the sensor module (4120), and extract standard location information matching the corresponding vision data (DB vector data).
  • the first location information extraction module (4140) may include at least one of a data input unit (4141), a location group selection unit (4142), a similarity result calculation unit (4143), and a location information provision unit (4144), as illustrated in FIG. 21.
  • the above data input unit (4141) can receive current vector data from the vision module (4130) and input estimated location information and specific location group information (location group ID or identification information) from the sensor module (4120), respectively. Accordingly, information on the image currently being captured through the vision module (4130), location information recognized through the sensor module (4120), and information on the location group corresponding to the location information can be input, respectively.
  • the above location group selection unit (4142) can select a location group stored in the first DB module (4110) according to the location group information (location group ID or identification information) input through the data input unit (4141).
  • location group information location group ID or identification information
  • candidate images to be compared with the image recognized by the vision module (4130) can be specified, thereby reducing the number of comparison targets. Accordingly, since the number of comparisons is reduced, the time required for the similarity comparison process can be reduced, and errors such as cases where images are similar but located in different places can be eliminated, thereby increasing accuracy.
  • the above similarity result producing unit (4143) can perform a cosine similarity measurement between the 'DB vector data' belonging to the location group selected through the location group selecting unit (4142) and the 'current vector data' input through the data input unit (4141) and produce a score accordingly.
  • the above location information providing unit (4144) checks whether the highest similarity score among the similarity scores calculated through the similarity result calculating unit (4143) exceeds a preset threshold score, and if the highest similarity score exceeds the threshold score, extracts and provides standard location information that matches the DB vector data that received the highest similarity score.
  • the above location information reset module (4150) can compare the standard location information extracted through the first location information extraction module (4140) and the estimated location information calculated through the sensor module (4120), and reset the location information of the autonomous driving robot (4100) based on the comparison result.
  • the location information reset module (4150) compares the estimated location information produced by the sensor module (4120) with the standard location information provided by the location information provider (4144) to determine whether they match, and if they do not match, determines that there is an abnormality in the driving status of the autonomous driving robot (4100), resets the LiDAR and Odometry of the sensor module (3000) while the autonomous driving robot (4100) is temporarily stopped, and then sets the standard location information provided by the location information provider (4144) as the initial location information of the LiDAR and Odometry to correct the location error.
  • This location information reset module (4150) performs a similarity check between an image acquired in real time through visual loop detection and a pre-built database, and can readjust the initial location information from LiDAR and Odometry based on the location information estimated using visual information when there is an abnormality in the driving status of the autonomous driving robot (4100).
  • the above DB update module (4160) adds the image of the current vector data having the highest similarity score to the candidate DB if the highest similarity score does not exceed a threshold, and then performs a similarity comparison with vector data previously added to the candidate DB. If the image exceeds the threshold, an up-count is performed on the image ID of the corresponding vector data. If the image does not exceed the threshold, a new image ID is assigned and added to the candidate DB.
  • the average value of the current vector data having the highest similarity score and the estimated location information corresponding to the current vector data is additionally registered in the first DB module (4110), so as to update the data set of the first DB module (4110).
  • the above second DB module (4210) is a component that stores a plurality of pre-generated vision data (DB vector data) and a plurality of standard location information by matching them with each other, and stores them as a plurality of location groups each grouped according to a set area having a certain coordinate range, and has more data (data set including DB vector data and standard location information) than the first DB module (4110) installed in the autonomous driving robot (4100), so that when the autonomous driving robot (4100) is unable to perform the first location estimation, the cloud server (4200) can perform the second location estimation for the autonomous driving robot (4100).
  • This second DB module (4210) is constructed with the same data structure as the above-described first DB module (4210), but by utilizing the advantages of the cloud, more data is constructed so as to provide high accuracy for location estimation.
  • the second location information extraction module (4220) above can extract standard location information that matches the vision data most similar to the vision recognition result among the vision data belonging to a specific location group through the sensor module (4120) of the autonomous driving robot (4100) when the standard location information is not extracted through the first location information extraction module (4140) of the autonomous driving robot (4100) (i.e., when the location estimation is not possible in the autonomous driving robot (4100), i.e., perform a second location estimation process, and extract the standard location information extracted accordingly and provide it to the first location information extraction module (4140) of the autonomous driving robot (4100).
  • the DB can be updated through the DB automatic update algorithm.
  • These cloud servers (4200) continuously receive and accumulate data on driving records, judgment results, etc. of a plurality of autonomous driving robots (4100) and form a composite DB, thereby helping to determine the driving status of the autonomous driving robot (4100) through virtual driving and recommending a driving route while the robot is driving.

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Abstract

La présente invention concerne un appareil et un système permettant de corriger une erreur de position pour mettre en œuvre une technologie SLAM (cartographie et localisation simultanées). La présente invention concerne, par exemple, un appareil comprenant : un module DB pour mettre en correspondance et stocker de multiples données visuelles pré-générées et de multiples informations de position standard, les données étant stockées dans de multiples groupes de position qui sont regroupés, respectivement, en fonction de zones de configuration ayant une plage de coordonnées prédéterminée ; un module de détection pour calculer des informations de position estimées d'un robot à conduite autonome via LiDAR et odométrie, et spécifier un groupe de position parmi les multiples groupes de position en fonction des informations de position estimées calculées ; un module de vision pour fournir un résultat de reconnaissance visuelle par l'intermédiaire d'un processus de reconnaissance visuelle d'une image de conduite du robot à conduite autonome ; un module d'extraction d'informations de position pour extraire les informations de position standard correspondant aux données visuelles les plus similaires au résultat de la reconnaissance visuelle parmi les données visuelles appartenant au groupe de positions spécifié par le module de détection ; et un module de reconfiguration d'informations de position pour comparer les informations de position standard extraites par le module d'extraction d'informations de position avec les informations de position estimées calculées par le module de détection, et reconfigurer et corriger les informations de position du robot à conduite autonome en fonction d'un résultat de comparaison.
PCT/KR2024/007583 2023-06-08 2024-06-03 Appareil et système permettant de corriger une erreur de position par analyse et comparaison en temps réel entre des données de vision et des données lidar pour mettre en oeuvre une technologie slam Ceased WO2024253400A1 (fr)

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KR10-2023-0073292 2023-06-08
KR1020230073293A KR102631315B1 (ko) 2023-06-08 2023-06-08 Slam 기술 구현을 위한 비전 데이터 및 라이다 데이터 간의 실시간 분석 및 대조를 통해 위치 오차 보정이 가능한 시스템
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