WO2024244657A1 - 机器人运动控制的方法、装置、机器人和存储介质 - Google Patents

机器人运动控制的方法、装置、机器人和存储介质 Download PDF

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Publication number
WO2024244657A1
WO2024244657A1 PCT/CN2024/084443 CN2024084443W WO2024244657A1 WO 2024244657 A1 WO2024244657 A1 WO 2024244657A1 CN 2024084443 W CN2024084443 W CN 2024084443W WO 2024244657 A1 WO2024244657 A1 WO 2024244657A1
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Prior art keywords
point cloud
robot
laser point
cloud frame
cluster
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PCT/CN2024/084443
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English (en)
French (fr)
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WO2024244657A9 (zh
WO2024244657A8 (zh
Inventor
陈俊伟
黄寅
周晓帆
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Shenzhen Pudu Technology Co Ltd
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Shenzhen Pudu Technology Co Ltd
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Priority to KR1020257040840A priority Critical patent/KR20260007350A/ko
Priority to EP24813863.8A priority patent/EP4714616A4/en
Publication of WO2024244657A1 publication Critical patent/WO2024244657A1/zh
Anticipated expiration legal-status Critical
Publication of WO2024244657A8 publication Critical patent/WO2024244657A8/zh
Publication of WO2024244657A9 publication Critical patent/WO2024244657A9/zh
Ceased legal-status Critical Current

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Classifications

    • 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
    • G05D1/244—Arrangements for determining position or orientation using passive navigation aids external to the vehicle, e.g. markers, reflectors or magnetic means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1656—Program controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Program controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J13/00—Controls for manipulators
    • B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
    • B25J19/02—Sensing devices
    • B25J19/021—Optical sensing devices
    • B25J19/022—Optical sensing devices using lasers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1602—Program controls characterised by the control system, structure, architecture
    • 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
    • G05D1/242—Means based on the reflection of waves generated by the vehicle
    • 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
    • G05D1/60—Intended control result
    • G05D1/617—Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2107/00—Specific environments of the controlled vehicles
    • G05D2107/60—Open buildings, e.g. offices, hospitals, shopping areas or universities
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2109/00—Types of controlled vehicles
    • G05D2109/10—Land vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2111/00—Details of signals used for control of position, course, altitude or attitude of land, water, air or space vehicles
    • G05D2111/10—Optical signals
    • G05D2111/17—Coherent light, e.g. laser signals

Definitions

  • the present application relates to the technical field of mobile robots, and in particular to a method, device, robot, storage medium and computer program product for robot motion control.
  • the robot may enter dangerous areas or prohibited areas during movement. For example, the robot may be at risk of falling due to entering a dangerous area.
  • the robot can be controlled by infrared detection, but because the sensing distance of infrared detection is short, when the robot moves at a fast speed, it cannot prevent the robot from falling or may cause the robot to topple over due to emergency braking.
  • the sensing distance of the magnetic strips is short and it is easy to demagnetize.
  • the anti-fall method based on depth cameras has poor accuracy and is prone to false detection. Therefore, how to ensure the safe operation of the robot has become an urgent problem to be solved.
  • a method, an apparatus, a robot, a computer-readable storage medium, and a computer program product for controlling robot motion are provided.
  • a method for controlling robot motion wherein the robot is equipped with a laser radar and a marker is arranged in the operating environment of the robot, the method comprising:
  • the robot is motion controlled.
  • a device for controlling robot motion comprising:
  • An acquisition module used to acquire a laser point cloud frame obtained by scanning the operating environment
  • a filtering module is used to filter the laser point cloud frame according to the reflection characteristics corresponding to each data point in the laser point cloud frame. Filter the data points in to obtain a filtered laser point cloud frame;
  • a clustering module used for clustering the data points in the filtered laser point cloud frame to obtain point cloud clusters
  • a control module is used to control the motion of the robot if it is determined that the laser point cloud frame contains a marker based on the point cloud cluster.
  • a robot comprises a memory and a processor, wherein the memory stores a computer program, the robot is equipped with a laser radar, markers are arranged in the operating environment of the robot, and the processor implements the steps of a method for controlling the motion of the robot when executing the computer program.
  • a computer-readable storage medium stores a computer program, which implements the steps of the robot motion control method when executed by a processor.
  • a computer program product comprising a computer program, which implements the steps of the robot motion control method when executed by a processor.
  • FIG1 is a diagram showing an application environment of a method for controlling robot motion in one embodiment
  • FIG2 is a schematic flow chart of a method for controlling robot motion in one embodiment
  • FIG3 is a schematic diagram of a reflective marker in one embodiment
  • FIG4 is a schematic diagram of a flow chart of a method for controlling the motion of a robot according to distance in one embodiment
  • FIG5a is a schematic diagram of an area determined according to a distance from a marker in one embodiment
  • FIG5 b is a schematic diagram of a process of filtering a laser point cloud frame in one embodiment
  • FIG6 is a schematic diagram of a process of a method for clustering point cloud clusters in one embodiment
  • FIG7 is a schematic flow chart of a method for controlling robot motion in another embodiment
  • FIG8 is a block diagram of a robot motion control device according to an embodiment
  • FIG. 9 is a diagram showing the internal structure of a robot in one embodiment.
  • the method for robot motion control provided in the embodiment of the present application can be applied in the application environment shown in FIG. 1.
  • the robot 102 obtains a laser point cloud frame obtained by scanning the operating environment; according to the reflection characteristics corresponding to each data point in the laser point cloud frame, the data points in the laser point cloud frame are filtered to obtain a filtered laser point cloud frame; the data points in the filtered laser point cloud frame are clustered to obtain a point cloud cluster; if it is determined based on the point cloud cluster that the laser point cloud frame contains a marker, the robot 102 is motion controlled.
  • the robot 102 can be, but is not limited to, various delivery robots, operating robots, service robots, sorting robots, or cleaning robots.
  • the user sets a marker at the edge of a dangerous area or a prohibited area in the operating environment of the robot 102, and the marker has at least one of an identifiable structural feature, a material feature, or a pattern feature.
  • the user can paste markers on both sides of the stairs, such as reflective markers or structured markers, and the reflective markers have a specific shape and a high reflectivity to the laser pulse; and the structured markers have a specific structure, and in the laser point cloud frame formed after the laser pulse is reflected, a specific structural feature is presented.
  • the robot 102 is equipped with a laser radar, which scans the operating environment to obtain a laser point cloud frame, and then extracts the reflection intensity or structural characteristics as reflection features to detect whether there are markers in the operating environment, thereby controlling the robot's motion.
  • a method for robot motion control is provided, which is described by taking the method applied to the robot in FIG1 as an example, and includes the following steps:
  • the operating environment is the environment in which the robot operates, which may be an indoor environment or an outdoor environment.
  • the operating environment may be a road environment for delivering goods.
  • the operating environment may be a hotel environment for providing services.
  • the operating environment may be an operating environment in a factory.
  • the operating environment is provided with markers, including reflective markers and structured markers.
  • the reflective marker has a high reflection intensity to the laser pulse. According to the reflection intensity corresponding to each data point, the data points with a high reflection intensity can be filtered out to identify the reflective marker from the filtered data points.
  • the structured marker has a specific structure, and in the laser point cloud frame formed after the reflection of the laser pulse, it presents a specific structural feature. Therefore, it can be filtered according to the structural features corresponding to each data point to identify the structured marker from the filtered data points.
  • the robot is equipped with a laser radar, so it can be scanned by the laser radar to obtain a laser point cloud frame.
  • the laser point cloud frame consists of multiple data points, each of which has a corresponding laser intensity.
  • the laser radar is an optical sensor that can emit laser pulses into the operating environment and receive laser pulses reflected by various objects in the operating environment, and generate a laser point cloud frame based on the reflected laser pulses.
  • the data point is a point in the laser point cloud frame, which is used to describe the point in the three-dimensional space scanned by the laser radar.
  • Each data point includes multiple attributes such as position coordinates, reflection characteristics, scanning angle, etc.
  • the reflection characteristics can be reflection intensity or structural characteristics of reflection.
  • the reflection intensity is used to represent the pulse echo intensity of the laser radar. When the reflectivity of the point in the three-dimensional space to the laser pulse is higher, the reflection intensity of the data point corresponding to the point in the three-dimensional space is higher.
  • the structural feature is the structured information presented by the pulse echo of the laser radar, which can reflect the specific structure of each object in the operating environment.
  • S204 specifically includes: determining an intensity threshold according to the reflection intensity corresponding to each data point in the laser point cloud frame; filtering out data points whose reflection intensity is greater than the intensity threshold among the data points in the laser point cloud frame to obtain a filtered laser point cloud frame.
  • a point cloud cluster is a cluster composed of similar data points.
  • a point cloud cluster can be a cluster composed of data points corresponding to the same object scanned by a laser radar.
  • Clustering is an unsupervised learning technology that measures the similarity of clustered objects and clusters similar objects into one category.
  • the robot can determine whether the laser point cloud frame contains a reflective marker based on the point cloud cluster. If it is determined based on the point cloud cluster that the laser point cloud frame contains a reflective marker, the robot is motion controlled.
  • the reflective marker is a marker made of reflective material, which is pasted on the edge of a dangerous area or a prohibited area and can be composed of graphic elements of various shapes.
  • the reflective marker can be composed of one or more rectangles, circles, ovals or triangles.
  • the multiple graphic elements that make up the reflective marker can be the same or different.
  • the reflective marker can be composed of two or more rectangles, or the reflective marker can be composed of a rectangle and a circle, or the reflective marker can be composed of a circle and a triangle.
  • the reflective marker is composed of two rectangles arranged side by side.
  • the size of the rectangle and the spacing between the rectangles can be adjusted according to actual needs.
  • the size of the rectangle can be 50 mm ⁇ 100 mm
  • the spacing between the two rectangles can be 50 mm.
  • the robot contains reflective markers in the laser point cloud frame collected by the laser radar, it means that the robot is about to run into a prohibited area and the robot is motion controlled.
  • users can also paste reflective markers on the edge of fragile obstacles such as glass.
  • the robot uses the laser point cloud frame collected by the laser radar, it When the light point cloud frame contains reflective markers, it means that the robot may collide with fragile obstacles and control the robot's motion.
  • the robot can determine whether the laser point cloud frame contains a structured marker based on the point cloud cluster. If it is determined based on the point cloud cluster that the laser point cloud frame contains a structured marker, the robot is motion controlled.
  • a laser point cloud frame obtained by scanning the operating environment is obtained; since the reflection characteristics of the marker to the laser are different from the reflection characteristics of other objects in the operating environment, the data points in the laser point cloud frame are filtered according to the reflection characteristics corresponding to each data point in the laser point cloud frame, so that the data points with reflection characteristics different from other objects can be filtered out to obtain a filtered laser point cloud frame.
  • the data points in the filtered laser point cloud frame are clustered to obtain a point cloud cluster, so that it can be judged whether the laser point cloud frame contains a marker according to the shape characteristics of the marker. If it is determined based on the point cloud cluster that the laser point cloud frame contains a marker, the robot is controlled in motion.
  • the recognition accuracy is higher, and the robot is effectively prevented from entering the dangerous area and the prohibited area.
  • the laser sensing distance is far, and the marker can be identified at a long distance, so that the robot has sufficient time to brake, effectively preventing the fast-moving robot from falling or tipping over due to emergency braking, and ensuring the safe operation of the robot.
  • S208 specifically includes the following steps:
  • the distance is the distance between the robot and the marker in three-dimensional space.
  • the robot can obtain the time interval between the laser radar emitting the laser pulse and receiving the reflected echo, and the distance between the robot and the marker can be determined based on the time interval.
  • the robot's motion is controlled according to the distance between the robot and the marker.
  • S404 specifically includes: determining the area where the robot is currently located according to the distance, and controlling the movement of the robot according to the area where the robot is currently located.
  • the black rectangular frame is a reflective marker.
  • Area A is a braking area
  • area B is an avoidance area
  • area C is a deceleration area.
  • the robot moves to the deceleration area, the robot is controlled to reduce the moving speed, for example, the moving speed of the robot is reduced to 0.6 m/s; when the robot is in the avoidance area, the robot is controlled to stop moving when encountering an obstacle; when the robot is in the braking area, the robot is controlled to stop moving.
  • the deceleration area is farthest from the reflective marker
  • the avoidance area is between the braking area and the deceleration area
  • the braking area is closest to the reflective marker.
  • the distance between each area and the reflective marker can be adjusted.
  • the braking area can be an area with a distance less than 1.2 meters from the reflective marker
  • the avoidance area can be an area with a distance greater than or equal to 1.2 meters and less than 1.5 meters from the reflective marker
  • the deceleration area can be an area with a distance greater than or equal to 1.5 meters and less than 2 meters from the reflective marker.
  • S404 specifically includes: controlling the movement path of the robot according to the distance. When the distance is less than the preset value, the robot is controlled to stop moving and return. Or when the distance is less than the preset value, the robot is controlled to adjust the moving direction.
  • the robot's movement mode can be adjusted according to the distance, thereby improving the flexibility of the robot's movement.
  • S208 specifically includes: if it is determined based on the point cloud cluster that the laser point cloud frame contains a marker, then the position coordinates corresponding to each marker are determined respectively; if the position coordinates corresponding to the markers in at least two frames of laser point cloud frames are the same, the robot is motion controlled.
  • the position coordinates are the coordinates of the marker in the world coordinate system.
  • Each data point in the laser point cloud frame includes the position coordinates corresponding to the data point.
  • the robot can determine the position coordinates of the marker based on the position coordinates corresponding to each data point. To avoid false detection by the robot, when the robot recognizes a marker in a certain laser point cloud frame, the robot continues to collect laser point cloud frames through the laser radar. If the position coordinates corresponding to the markers in multiple laser point cloud frames continuously collected by the laser radar are the same, it means that these markers all correspond to markers at the same position. Then, it is determined that the robot has scanned the marker and the robot is controlled in motion.
  • the position coordinates of each marker are determined respectively; if the position coordinates of the markers in at least two laser point cloud frames are the same, the robot is controlled in motion. This can effectively avoid robot misdetection and improve the accuracy of robot motion control.
  • the marker includes a reflective marker
  • the reflection characteristic includes reflection intensity
  • the average reflection intensity is the average value of the reflection intensity corresponding to each data point in the laser point cloud frame.
  • the maximum reflection intensity is the maximum value of the reflection intensity corresponding to each data point in the laser point cloud frame.
  • S504 Determine an intensity threshold according to the average reflection intensity and the maximum reflection intensity.
  • the robot can determine the intensity threshold according to the average reflection intensity and the maximum reflection intensity, and then filter the data points in the laser point cloud frame according to the intensity threshold.
  • S504 specifically includes: performing weighted summation of the average reflection intensity and the maximum reflection intensity, and using the sum as the intensity threshold.
  • the weight values corresponding to the average reflection intensity and the maximum reflection intensity may be the same or different. For example, the weight value corresponding to the average reflection intensity is 0.3, and the weight value corresponding to the maximum reflection intensity is 0.7.
  • the developer can configure the weight values corresponding to the average reflection intensity and the maximum reflection intensity when the robot leaves the factory, or the user can also set it in the setting interface.
  • S504 specifically includes: the robot determines an average value of the average reflection intensity and the maximum reflection intensity, and uses the average value as the intensity threshold.
  • the robot filters the data points in the laser point cloud frame based on the intensity threshold, deletes the data points with reflection intensity lower than the intensity threshold, and retains the data points with reflection intensity higher than the intensity threshold.
  • the average reflection intensity and the maximum reflection intensity of the data points are determined according to the reflection intensity corresponding to each data point in the laser point cloud frame; the intensity threshold is determined according to the average reflection intensity and the maximum reflection intensity; based on the intensity threshold, the data points in the laser point cloud frame are filtered to obtain a filtered laser point cloud frame.
  • the data points that meet the reflection characteristics of the reflective marker can be filtered out from the laser point cloud frame, and the characteristics of the high reflection intensity of the laser pulse by the reflective marker can be used to identify the dangerous area, thereby improving the accuracy of the robot motion control.
  • the marker is a graphic combination consisting of at least two graphic elements; as shown in FIG6 , S206 specifically includes the following steps:
  • the graphic elements can be graphics of various shapes, including rectangles, circles or triangles.
  • the robot can identify the graphic combination in the laser point cloud frame according to the shape characteristics of the graphic combination.
  • the first clustering threshold determined by the robot is less than the distance between the graphic elements
  • the second clustering threshold is greater than the distance between the graphic elements, so that the data points corresponding to the graphic elements can be clustered into clusters through the first clustering threshold, and the data points corresponding to the graphic combination can be clustered into clusters through the second clustering threshold.
  • the robot can determine the first clustering threshold to be 0.5R and the second clustering threshold to be 1.5R.
  • the first shape condition is a screening condition determined based on the shape of the graphic element, which is used to screen out clusters that meet the shape characteristics of the graphic element.
  • the first shape condition can be that the aspect ratio of the minimum circumscribed rectangle of the cluster is within a preset range.
  • the preset range corresponding to the aspect ratio can be determined according to the aspect ratio of the graphic element. For example, if the graphic element is a rectangle with an aspect ratio equal to 2, the preset range can be a numerical range of 1.8 to 2.2.
  • the first shape condition can be that the radius of the minimum circumscribed circle of the cluster is within a preset range. The preset range corresponding to the radius can be determined according to the radius of the graphic element.
  • the preset range can be a numerical range of 2.5 to 3.5.
  • the robot clusters the data points in the filtered laser point cloud frame according to the first clustering threshold, and clusters the data points whose distances are less than the first clustering threshold into a cluster. Then, the clusters obtained by clustering are screened according to the first shape condition, and clusters that are too large, too small, or have dissimilar shapes compared to the graphic element are discarded. The first cluster obtained meets the geometric characteristics of a single graphic element. ⁇
  • the second shape condition is a screening condition determined based on the shape of the graphic combination, which is used to screen out clusters that meet the shape characteristics of the graphic combination.
  • the second shape condition may be that the aspect ratio of the minimum circumscribed rectangle of the cluster is within a preset range.
  • the preset range of the aspect ratio may be determined based on the aspect ratio of the graphic combination as a whole. For example, if the aspect ratio of the graphic combination is equal to 1.5, the preset range may be a numerical interval of 1 to 2.
  • the second shape condition may be that the radius of the minimum circumscribed circle of the cluster is within a preset range.
  • the preset range of the radius may be determined based on the radius of the graphic combination as a whole.
  • the robot clusters the data points in the first cluster according to the second clustering threshold, and clusters the data points whose distances are less than the second clustering threshold into one cluster. Then, the clusters obtained by clustering are screened according to the second shape condition, and clusters that are too large, too small, or have shapes that are not similar to the graphic combination are discarded, so as to obtain a second cluster that meets the overall geometric characteristics of the graphic combination.
  • the robot filters the second class cluster to obtain the point cloud cluster.
  • S608 specifically includes: clustering each second cluster into multiple subclusters according to the first clustering threshold, and determining the number of subclusters in each second cluster; filtering the second clusters based on the number of subclusters in the second clusters to obtain point cloud clusters.
  • the robot clusters each second-class cluster according to the first clustering threshold, and clusters each second-class cluster into multiple subclusters, whose shapes and sizes are similar to the graphic elements.
  • the robot determines the number of subclusters in each second-class cluster, compares the number of subclusters in the second-class cluster with the number of graphic elements in the graphic combination, discards the second-class clusters with too many or too few subclusters in the second-class cluster, and uses the retained second-class clusters as point cloud clusters. For example, if the image combination includes two graphic elements, the second-class clusters with less than 2 or more than 3 subclusters in the second-class cluster are discarded, so that the retained point cloud clusters contain 2-3 subclusters.
  • the robot filters the second-class clusters according to the number of subclusters in the second-class clusters, so that the filtered point cloud clusters not only meet the geometric characteristics of the graphic combination, but also the number of subclusters in the second-class clusters contained is consistent with the number of graphic elements in the graphic combination, so that the markers can be identified more accurately and false detection can be avoided.
  • the first clustering threshold and the second clustering threshold are determined according to the distance between each graphic element in the graphic combination, and then the data points in the filtered laser point cloud frame are clustered according to the first clustering threshold, and the first cluster that meets the first shape condition is selected from the clusters obtained by clustering.
  • the second clustering threshold the data points in the first cluster are clustered, and the second cluster that meets the second shape condition is selected from the clusters obtained by clustering.
  • the second cluster is filtered to obtain a point cloud cluster. In this way, a point cloud cluster that meets the geometric characteristics of the image combination can be clustered, and the marker can be identified according to the shape of the image combination, thereby improving the accuracy of identifying the marker.
  • S208 specifically includes: selecting at least two target subclusters in the subclusters of each point cloud cluster; for each point cloud cluster, determining the ratio between the number of data points in the target subclusters; if there is a target ratio in the ratio that satisfies the ratio condition, determining that a marker is included in the laser point cloud frame, and performing motion control on the robot.
  • the target sub-cluster is the sub-cluster that meets the selection criteria among all sub-clusters.
  • the selection criteria can be the number of data points.
  • the selection condition may be that the number of data points in all subclusters is ranked within a preset rank, and the preset rank may be 2, for example.
  • the ratio condition is a condition for judging whether a point cloud cluster is a landmark according to the ratio.
  • the ratio condition may be that the ratio is less than a preset value, for example, the preset value may be 0.5, 0.6, etc.
  • the ratio condition may be that the ratio is within a preset ratio interval.
  • the robot may determine the preset ratio interval according to the size of each graphic element in the graphic combination.
  • the robot may determine the preset ratio interval to be [0.5, 1.5]. If there is a target ratio in the ratio that satisfies the ratio condition, it means that each target sub-cluster in the point cloud cluster conforms to the size characteristics of each graphic element in the graphic combination, thereby determining that the point cloud cluster is a landmark.
  • At least two target subclusters are selected from each subcluster of the point cloud cluster; for each point cloud cluster, the ratio between the number of data points in the target subclusters is determined; if there is a target ratio that satisfies the ratio condition, it is determined that the laser point cloud frame contains a marker, and the robot is controlled to move. Whether the point cloud cluster is a marker is determined based on whether the size of each target subcluster in the point cloud cluster meets the size characteristics of each graphic element in the graphic combination, which further improves the accuracy of identifying the marker.
  • the method for controlling robot motion includes the following steps:
  • Each data point in the laser point cloud frame corresponds to a reflection feature, and the reflection feature may be a reflection intensity or a structural feature.
  • the reflection feature is the reflection intensity, S704 is executed.
  • S714 Cluster each second cluster into a plurality of subclusters according to the first clustering threshold, and determine the number of subclusters in each second cluster.
  • the laser point cloud frame contains a marker, and the distance between the robot and the marker is determined; the robot is controlled in motion according to the distance.
  • steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
  • the embodiment of the present application also provides a device for implementing a robot motion control corresponding to the method for robot motion control involved above.
  • the implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more robot motion control devices provided below can refer to the limitations of the robot motion control method above, and will not be repeated here.
  • a robot motion control device including: an acquisition module 802 , a filtering module 804 , a clustering module 806 and a control module 808 , wherein:
  • An acquisition module 802 is used to acquire a laser point cloud frame obtained by scanning the operating environment
  • the filtering module 804 is used to filter the data points in the laser point cloud frame according to the reflection characteristics corresponding to each data point in the laser point cloud frame to obtain a filtered laser point cloud frame;
  • a clustering module 806 is used to cluster the data points in the filtered laser point cloud frame to obtain point cloud clusters
  • the control module 808 is used to control the motion of the robot if it is determined based on the point cloud cluster that the laser point cloud frame contains a marker.
  • a laser point cloud frame obtained by scanning the operating environment is obtained; since the reflection characteristics of the marker to the laser are different from the reflection characteristics of other objects in the operating environment, the data points in the laser point cloud frame are filtered according to the reflection characteristics corresponding to each data point in the laser point cloud frame, so that the data points with reflection characteristics different from other objects can be filtered out to obtain a filtered laser point cloud frame.
  • the data points in the filtered laser point cloud frame are clustered to obtain a point cloud cluster, so that it can be judged whether the laser point cloud frame contains a marker according to the shape characteristics of the marker. If it is determined based on the point cloud cluster that the laser point cloud frame contains a marker, the robot is controlled in motion.
  • the recognition accuracy is higher, and the robot is effectively prevented from entering the dangerous area and the prohibited area.
  • the laser sensing distance is far, and the marker can be identified at a long distance, so that the robot has sufficient time to brake, effectively preventing the fast-moving robot from falling or tipping over due to emergency braking, and ensuring the safe operation of the robot.
  • control module 808 is further configured to:
  • the distance between the robot and the corresponding marker is determined
  • the laser point cloud frame includes at least two frames; the control module 808 is further used to:
  • the position coordinates corresponding to each marker are determined respectively;
  • the robot is motion controlled.
  • the filtering module 804 is further configured to:
  • the average reflection intensity and the maximum reflection intensity of the data point are determined
  • the data points in the laser point cloud frame are filtered to obtain a filtered laser point cloud frame.
  • the marker is a graphic combination consisting of at least two graphic elements; the clustering module 806 is further configured to:
  • Clustering the data points in the first cluster according to the second clustering threshold, and selecting a second cluster that meets a second shape condition from the clusters obtained by clustering; the second shape condition is determined based on the shape of the graphic combination;
  • the second type of clusters are filtered to obtain point cloud clusters.
  • the clustering module 806 is further configured to:
  • each second-class cluster into a plurality of sub-clusters according to a first clustering threshold, and determining the number of sub-clusters in each second-class cluster;
  • the second type of clusters are filtered based on the number of subclusters to obtain point cloud clusters.
  • control module 808 is further configured to:
  • For each point cloud cluster determine the ratio between the number of data points in the target sub-cluster
  • the robot is motion controlled.
  • Each module in the above-mentioned robot motion control device can be implemented in whole or in part by software, hardware and a combination thereof.
  • Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.
  • a robot in one embodiment, is provided, and its internal structure diagram can be shown in FIG9.
  • the robot includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input device.
  • the processor, the memory, and the input/output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input/output interface.
  • the processor of the robot is used to provide computing and control capabilities.
  • the memory of the robot includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system and a computer program.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the input/output interface of the robot is used to exchange information between the processor and the external device.
  • the communication interface of the robot is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies.
  • a method for controlling the motion of a robot is implemented.
  • the display unit of the robot is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device.
  • the display screen can be a liquid crystal display screen or an electronic ink display screen.
  • the input device of the robot can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the robot shell, or an external keyboard, touchpad or mouse.
  • FIG. 9 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied.
  • a specific robot may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
  • a robot including a memory and a processor, wherein a computer program is stored in the memory, the robot is equipped with a laser radar, and markers are set in the operating environment of the robot.
  • the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented.
  • a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.
  • a computer program product including a computer program, which implements the steps in the above method embodiments when executed by a processor.
  • user information including but not limited to user device information, user personal information, etc.
  • data including but not limited to data used for analysis, stored data, displayed data, etc.
  • any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory.
  • Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.
  • Volatile memory can include random access memory (RAM) or external cache memory, etc.
  • RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • the database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database.
  • the non-relational database may include a distributed
  • the processors involved in the various embodiments provided in this application may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

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Abstract

公开了一种机器人运动控制的方法、装置、机器人和存储介质，机器人上搭载有激光雷达，机器人的运行环境中设置有标志物，方法包括：获取对运行环境进行扫描所得的激光点云帧(S202)；根据激光点云帧中各数据点对应的反射特征，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧(S204)；对过滤后的激光点云帧中的数据点进行聚类，得到点云簇(S206)；若基于点云簇确定激光点云帧中包含标志物，则对机器人进行运动控制(S208)。

Description

机器人运动控制的方法、装置、机器人和存储介质
相关申请的交叉引用
本申请要求于2023年05月31日提交中国专利局、申请号为2023106389099、申请名称为“机器人运动控制的方法、装置、机器人和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及移动机器人技术领域，特别是涉及一种机器人运动控制的方法、装置、机器人、存储介质和计算机程序产品。
背景技术
随着移动机器人技术的发展，移动机器人的应用环境越来越复杂，机器人在移动过程中可能进入危险区域或禁止区域，例如机器人可能因进入危险区域而产生跌落风险。传统技术中，可以通过红外探测对机器人进行运动控制，但由于红外探测的感知距离较近，当机器人移动速度较快时，无法防止机器人跌落或者可能因紧急刹车而导致机器人倾倒。基于磁条对机器人进行运动控制的方法中，磁条的感应距离较短并且容易消磁。基于深度相机的防跌落方法的精度较差，容易产生误检。因此，怎样保障机器人安全运行成为亟待解决的问题。
发明内容
根据本申请的各种实施例，提供一种机器人运动控制方的法、装置、机器人、计算机可读存储介质和计算机程序产品。
一种机器人运动控制的方法，所述机器人上搭载有激光雷达，所述机器人的运行环境中设置有标志物，所述方法包括：
获取对运行环境进行扫描所得的激光点云帧；
根据所述激光点云帧中各数据点对应的反射特征，对所述激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧；
对所述过滤后的激光点云帧中的数据点进行聚类，得到点云簇；
若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制。
一种机器人运动控制的装置，所述装置包括：
获取模块，用于获取对运行环境进行扫描所得的激光点云帧；
过滤模块，用于根据所述激光点云帧中各数据点对应的反射特征，对所述激光点云帧 中的数据点进行过滤，得到过滤后的激光点云帧；
聚类模块，用于对所述过滤后的激光点云帧中的数据点进行聚类，得到点云簇；
控制模块，用于若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制。
一种机器人，所述机器人包括存储器和处理器，所述存储器存储有计算机程序，所述机器人上搭载有激光雷达，所述机器人的运行环境中设置有标志物，所述处理器执行所述计算机程序时实现所述机器人运动控制的方法的步骤。
一种计算机可读存储介质，所述计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现所述机器人运动控制的方法的步骤。
一种计算机程序产品，所述计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实现所述机器人运动控制的方法的步骤。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他实施例的附图。
图1为一个实施例中机器人运动控制的方法的应用环境图；
图2为一个实施例中机器人运动控制的方法的流程示意图；
图3为一个实施例中反光标志物的示意图；
图4为一个实施例中根据距离对机器人进行运动控制方法的流程示意图；
图5a为一个实施例中根据与标志物间的距离确定的区域的示意图；
图5b为一个实施例中对激光点云帧进行过滤步骤的流程示意图；
图6为一个实施例中聚类得到点云簇的方法的流程示意图；
图7为另一个实施例中机器人运动控制的方法的流程示意图；
图8为一个实施例中机器人运动控制的装置的结构框图；
图9为一个实施例中机器人的内部结构图。
具体实施方式
为了便于理解本申请，下面将参照相关附图对本申请进行更全面的描述。附图中给出了本申请的较佳实施例。但是，本申请可以以许多不同的形式来实现，并不限于本文所描述的实施例。相反地，提供这些实施例的目的是使对本申请的公开内容的理解更加透彻全 面。
除非另有定义，本文所使用的所有的技术和科学术语与属于发明的技术领域的技术人员通常理解的含义相同。本文中在发明的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在限制本申请。本文所使用的术语“和/或”包括一个或多个相关的所列项目的任意的和所有的组合。
本申请实施例提供的机器人运动控制的方法，可以应用于如图1所示的应用环境中。其中，机器人102获取对运行环境进行扫描所得的激光点云帧；根据激光点云帧中各数据点对应的反射特征，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧；对过滤后的激光点云帧中的数据点进行聚类，得到点云簇；若基于点云簇确定激光点云帧中包含标志物，则对机器人102进行运动控制。其中，机器人102可以但不限于是各种配送机器人、作业机器人、服务机器人、分拣机器人或者清洁机器人等。用户在机器人102的运行环境中的危险区域或禁止区域的边缘设置标志物，该标志物具备可识别的结构特征、材料特征或者图案特征中的至少一个。例如，用户可以在楼梯的两侧粘贴标志物，如反光标志物或结构化标志物，反光标志物具有特定的形状，并且对激光脉冲的反射率较高；而结构化标志物具有特定的结构，在对激光脉冲反射后所形成的激光点云帧中，呈现出特定的结构特征。机器人102上安装有激光雷达，通过激光雷达对运行环境进行扫描，得到激光点云帧，从而通过提取出反射强度或结构特征作为反射特征，以探测运行环境中是否有标志物，进而可以对机器人进行运动控制。
在一个实施例中，如图2所示，提供了一种机器人运动控制的方法，以该方法应用于图1中的机器人为例进行说明，包括以下步骤：
S202，获取对运行环境进行扫描所得的激光点云帧。
其中，运行环境为机器人运行的环境，可以为室内环境或者是室外环境。例如，运行环境可以是配送货物的道路环境。又例如，运行环境可以是提供服务的酒店环境。又例如，运行环境可以是工厂内的作业环境。
该运行环境中设置有标志物，该标志物包括反光标志物和结构化标志物。其中，反光标志物对激光脉冲的反射强度较高，根据各数据点对应的反射强度，可以将反射强度较高的数据点过滤出来，以从过滤出的数据点中识别反光标志物。而结构化标志物具有特定的结构，在对激光脉冲反射后所形成的激光点云帧中，呈现出特定的结构特征，因此可以根据各数据点对应的结构特征进行过滤，以从过滤出的数据点中识别出结构化标志物。
该机器人上搭载有激光雷达，因此可以通过激光雷达进行扫描，得到激光点云帧。该激光点云帧由多个数据点组成，各数据点具有对应的激光强度。激光雷达是光学传感器，可以向运行环境中发射激光脉冲，并接收运行环境中的各物体反射回来的激光脉冲，根据反射回来的激光脉冲生成激光点云帧。
S204，根据激光点云帧中各数据点对应的反射特征，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧。
其中，数据点是激光点云帧中的点，用于描述激光雷达扫描到的三维空间中的点。每个数据点包括位置坐标、反射特征、扫描角度等多种属性。反射特征可以是反射强度或反射的结构特征。该反射强度用于表示激光雷达的脉冲回波强度，当三维空间中的点对激光脉冲的反射率越高时，该三维空间中的点对应的数据点的反射强度越高。该结构特征为激光雷达的脉冲回波所呈现出的结构化信息，可以反映出运行环境中各物体的特定结构。
在一个实施例中，当反射特征为反射强度时，S204具体包括：根据激光点云帧中各数据点对应的反射强度确定强度阈值；在激光点云帧的数据点中，过滤出反射强度大于强度阈值的数据点，得到过滤后的激光点云帧。
S206，对过滤后的激光点云帧中的数据点进行聚类，得到点云簇。
其中，点云簇是由同类数据点组成的簇。例如，点云簇可以是由激光雷达扫描的同一个物体对应的数据点组成的簇。聚类是无监督学习技术，对被聚类的对象进行相似度度量，将相似的对象聚为一类。
在一个实施例中，机器人可以通过K-Means(K均值)聚类算法、BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies，综合的层次聚类)算法或者高斯混合聚类算法，对过滤后的激光点云帧中的数据点进行聚类，得到点云簇。
S208，若基于点云簇确定激光点云帧中包含标志物，则对机器人进行运动控制。
在一个实施例中，当反射特征为反射强度时，机器人可以根据点云簇判断激光点云帧中是否包含反光标志物，若基于点云簇确定激光点云帧中包含反光标志物，则对机器人进行运动控制。
其中，反光标志物是由反光材料组成的标志物，粘贴在危险区域或禁止区域的边缘，可以由各种形状的图形元素组成。例如，反光标志物可以由一个或多个矩形、圆形、椭圆形或者三角形等组成。组成反光标志物的多个图形元素可以相同也可以不同。例如，反光标志物可以由两个或多个矩形组成，或者反光标志物也可以由一个矩形和一个圆形组成，或者反光标志物也可以由圆形和三角形组成。在一个实施例中，如图3所示，反光标志物由两个并排排列的矩形组成。矩形的大小以及矩形间的间距可以根据实际需求进行调整，例如矩形的大小可以为50毫米×100毫米，两个矩形间的间距可以为50毫米。
运动控制为控制机器人的运动模式，包括控制机器人的移动速度、移动方向或者移动路径等。例如，控制机器人停止移动，或者控制机器人绕过危险区域，或者控制机器人降低移动速度等。若基于点云簇确定激光点云帧中包含反光标志物，说明机器人即将运行至危险区域或禁止区域，需要对机器人进行运动控制。例如，用户可以在楼梯或者电梯两侧粘贴反光标志物，当机器人通过激光雷达采集的激光点云帧中包含反光标志物时，说明机器人即将运行至楼梯或者电梯附近，有跌落风险，对机器人进行运动控制。又例如，用户可以在水域等禁行区域边缘粘贴反光标志物，当机器人通过激光雷达采集的激光点云帧中包含反光标志物时，说明机器人即将运行至禁行区域，对机器人进行运动控制。又例如，用户也可以在玻璃等易碎障碍物的边缘粘贴反光标志物，当机器人通过激光雷达采集的激 光点云帧中包含反光标志物时，说明机器人可能会与易碎障碍物发生碰撞，对机器人进行运动控制。
在另一个实施例中，当反射特征为结构特征时，机器人可以根据点云簇判断激光点云帧中是否包含结构化标志物，若基于点云簇确定激光点云帧中包含结构化标志物，则对机器人进行运动控制。
上述实施例中，获取对运行环境进行扫描所得的激光点云帧；由于标志物对激光的反射特征与运行环境中的其它物体的反射特征不同，因此根据激光点云帧中各数据点对应的反射特征，对激光点云帧中的数据点进行过滤，从而可以将反射特征不同于其它物体的数据点过滤出来，得到过滤后的激光点云帧。对过滤后的激光点云帧中的数据点进行聚类，得到点云簇，从而可以根据标志物的形状特征判断激光点云帧中是否包含标志物。若基于点云簇确定激光点云帧中包含标志物，则对机器人进行运动控制。通过利用标志物对激光反射不同的特征以及形状特征在激光点云帧中识别标志物，识别精度更高，有效避免了机器人进入危险区域和禁止区域。并且激光的感应距离较远，在较远处即可识别标志物，使机器人有充足的时间进行刹车，有效避免了快速移动的机器人跌落或者因紧急刹车而倾倒，保障了机器人的安全运行。
在一个实施例中，如图4所示，S208具体包括如下步骤：
S402，若基于点云簇确定激光点云帧中包含标志物，则确定机器人与标志物间的距离。
其中，距离为机器人与标志物在三维空间中的距离。机器人可以获取激光雷达从发射激光脉冲至接收到反射回波间的时间间隔，根据时间间隔可以确定机器人至标志物间的距离。
S404，根据距离对机器人进行运动控制。
由于标志物粘贴在危险区域的边缘，在机器人距离标志物较远时，发生危险的紧迫性较弱，而在机器人距离标志物较近时，发生危险的紧迫性更强，因此根据机器人与标志物间的距离对机器人进行运动控制。
在一个实施例中，S404具体包括：根据距离确定机器人当前所在的区域，根据机器人当前所在的区域对机器人进行运动控制。具体地，如图5a所示，黑色矩形框为反光标志物。区域A为刹车区域，区域B为避停区域，区域C为减速区域。当机器人移动至减速区域时，控制机器人降低移动速度，例如将机器人的移动速度降至0.6米/秒；当机器人在避停区域时，控制机器人在遇到障碍物时停止移动；当机器人在刹车区域时，控制机器人停止移动。其中，减速区域距离反光标志物最远，避停区域在刹车区域与减速区域中间，刹车区域距离反光标志物最近。各区域与反光标志物间的距离可以进行调整，例如，刹车区域可以为与反光标志物间距离小于1.2米的区域，避停区域可以为与反光标志物间距离大于或等于1.2米且小于1.5米的区域，减速区域可以为与反光标志物间距离大于或等于1.5米且小于2米的区域。
在一个实施例中，S404具体包括：根据距离控制机器人的移动路径。例如，当距离 小于预设值时，控制机器人停止移动并返回。或者当距离小于预设值时，控制机器人调整移动方向。
上述实施例中，若基于点云簇确定激光点云帧中包含标志物，则确定机器人与标志物间的距离，并根据距离对机器人进行运动控制。从而可以根据距离调整机器人的运动模式，提高了机器人运动的灵活性。
在一个实施例中，S208具体包括：若基于点云簇确定激光点云帧中包含标志物，则分别确定各标志物对应的位置坐标；若至少两帧激光点云帧中的标志物对应的位置坐标相同，则对机器人进行运动控制。
其中，位置坐标为标志物在世界坐标系中的坐标。激光点云帧的各数据点中包括该数据点对应的位置坐标，机器人根据各数据点对应的位置坐标可以确定标志物的位置坐标。为避免机器人误检，当机器人在某一激光点云帧中识别到标志物时，使机器人继续通过激光雷达采集激光点云帧，若激光雷达连续采集的多个激光点云帧中的标志物对应的位置坐标相同，说明这些标志物均对应于同一位置的标志物，则确定机器人扫描到标志物，对机器人进行运动控制。
上述实施例中，若基于点云簇确定激光点云帧中包含标志物，则分别确定各标志物的位置坐标；若至少两帧激光点云帧中的标志物的位置坐标相同，则对机器人进行运动控制。从而可以有效避免机器人误检，提高了对机器人运动控制的准确性。
在一个实施例中，该标志物包括反光标志物，该反射特征包括反射强度；如图5b所示，S204具体包括如下步骤：
S502，根据激光点云帧中各数据点对应的反射强度，确定数据点的平均反射强度与最大反射强度。
其中，平均反射强度是激光点云帧中各数据点对应的反射强度的平均值。最大反射强度是激光点云帧中各数据点对应的反射强度的最大值。机器人在接收到激光点云帧时，对激光点云帧中各数据点对应的反射强度进行统计，计算平均反射强度，并在各数据点对应的反射强度中查找最大反射强度。机器人可以通过顺序查找法、折半查找法或者二叉树查找法等各种查找方法查找最大反射强度。
S504，根据平均反射强度与最大反射强度确定强度阈值。
当标志物为反光标志物时，由于反光标志物对激光脉冲的反射率较高，因此激光点云帧中的反光标志物对应的各数据点的反射强度较高。因此机器人可以根据平均反射强度与最大反射强度确定强度阈值，然后根据强度阈值对激光点云帧中的数据点进行过滤。
在一个实施例中，S504具体包括：对平均反射强度与最大反射强度进行加权求和，并将所得的和值作为强度阈值。其中，平均反射强度与最大反射强度对应的权重值可以相同，也可以不同。例如，平均反射强度对应的权重值为0.3，最大反射强度对应的权重值为0.7。开发人员可以在机器人出厂时对平均反射强度与最大反射强度对应的权重值进行配置，或者用户也可以在设置界面进行设置。
在一个实施例中，S504具体包括：机器人确定平均反射强度与最大反射强度的平均值，将平均值作为强度阈值。
S506，基于强度阈值，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧。
机器人基于强度阈值对激光点云帧中的数据点进行过滤，删除反射强度低于强度阈值的数据点，保留反射强度高于强度阈值的数据点。
上述实施例中，根据激光点云帧中各数据点对应的反射强度，确定数据点的平均反射强度与最大反射强度；根据平均反射强度与最大反射强度确定强度阈值；基于强度阈值，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧。从而可以从激光点云帧中过滤出符合反光标志物的反射特征的数据点，利用反光标志物对激光脉冲反射强度高的特征识别危险区域，提高了对机器人运动控制的准确性。
在一个实施例中，标志物为由至少两个图形元素组成的图形组合；如图6所示，S206具体包括如下步骤：
S602，根据图形组合中各图形元素间的距离确定第一聚类阈值和第二聚类阈值；第一聚类阈值小于第二聚类阈值。
其中，图形元素可以是各种形状的图形，包括矩形、圆形或者三角形等。机器人可以根据图形组合的形状特征在激光点云帧中识别图形组合。在一个实施例中，机器人确定的第一聚类阈值小于图形元素间的距离，第二聚类阈值大于图形元素间的距离，从而可以通过第一聚类阈值将图形元素对应的数据点聚为类簇，并通过第二聚类阈值将图形组合对应的数据点聚为类簇。在一个实施例中，假设图形元素间的距离为R，机器人可以确定第一聚类阈值为0.5R，第二聚类阈值为1.5R。
S604，根据第一聚类阈值，对过滤后的激光点云帧中的数据点进行聚类，并在聚类所得的类簇中选取满足第一形状条件的第一类簇；第一形状条件是基于图形元素的形状确定的。
其中，第一形状条件是基于图形元素的形状确定的筛选条件，用于筛选出符合图形元素形状特征的类簇。在一个实施例中，第一形状条件可以为类簇的最小外接矩形的长宽比在预设范围内。长宽比对应的预设范围可以是根据图形元素的长宽比确定的。例如，若图形元素为长宽比等于2的矩形，预设范围可以为1.8至2.2的数值区间。在另一个实施例中，第一形状条件可以为类簇的最小外接圆的半径在预设范围内。半径对应的预设范围可以是根据图形元素的半径确定的。例如，若图形元素为半径等于3的圆形，预设范围可以为2.5至3.5的数值区间。机器人根据第一聚类阈值对过滤后的激光点云帧中的数据点进行聚类，将相互间距离小于第一聚类阈值的数据点聚为一个类簇。然后根据第一形状条件对聚类所得的类簇进行筛选，舍弃与图形元素相比过大、过小或者形状不相似的类簇，所得的第一类簇符合单个图形元素的几何特征。、
S606，根据第二聚类阈值，对第一类簇中的数据点进行聚类，并在聚类所得的类簇中 选取满足第二形状条件的第二类簇；第二形状条件是基于图形组合的形状确定的。
其中，第二形状条件是基于图形组合的形状确定的筛选条件，用于筛选出符合图形组合形状特征的类簇。在一个实施例中，第二形状条件可以为类簇的最小外接矩形的长宽比在预设范围内。长宽比的预设范围可以是根据图形组合整体的长宽比确定的。例如，若图形组合的长宽比等于1.5，预设范围可以为1至2的数值区间。在另一个实施例中，第二形状条件可以为类簇的最小外接圆的半径在预设范围内。半径的预设范围可以是根据图形组合整体的半径确定的。
机器人根据第二聚类阈值对第一类簇中的数据点进行聚类，将相互间距离小于第二聚类阈值的数据点聚为一个类簇。然后根据第二形状条件对聚类所得的类簇进行筛选，舍弃与图形组合相比过大、过小或者形状不相似的类簇，得到符合图形组合整体几何特征的第二类簇。
S608，对第二类簇进行过滤，得到点云簇。
为了使最终所得的点云簇中所包含的类簇的数量与图形组合中所包含的图形元素的数量相一致，机器人对第二类簇进行过滤，得到点云簇。
在一个实施例中，S608具体包括：根据第一聚类阈值，将每个第二类簇聚类为多个子簇，并确定每个第二类簇中子簇的数量；基于第二类簇中子簇的数量对第二类簇进行过滤，得到点云簇。
机器人根据第一聚类阈值分别对每个第二类簇进行聚类，将每个第二类簇聚类为多个子簇，子簇的形状与大小与图形元素相似。机器人确定每个第二类簇中子簇的数量，将第二类簇中子簇的数量与图形组合中图形元素的数量进行对比，舍弃第二类簇中子簇的数量过多或过少的第二类簇，将保留的第二类簇作为点云簇。例如，若图像组合中包括两个图形元素，则舍弃掉第二类簇中子簇的数量少于2或者大于3的第二类簇，使保留的点云簇中包含2-3个子簇。机器人根据第二类簇中子簇的数量对第二类簇进行过滤，使过滤出的点云簇不仅符合图形组合的几何特征，而且所包含的第二类簇中子簇的数量与图形组合中图形元素的数量相一致，从而能够更加准确的识别标志物，避免发生误检。
上述实施例中，根据图形组合中各图形元素间的距离确定第一聚类阈值和第二聚类阈值，然后根据第一聚类阈值，对过滤后的激光点云帧中的数据点进行聚类，并在聚类所得的类簇中选取满足第一形状条件的第一类簇。根据第二聚类阈值，对第一类簇中的数据点进行聚类，并在聚类所得的类簇中选取满足第二形状条件的第二类簇。对第二类簇进行过滤，得到点云簇。从而可以聚类得到符合图像组合几何特征的点云簇，根据图像组合的形状识别标志物，提高了识别标志物的准确性。
在一个实施例中，S208具体包括：在每个点云簇的子簇中选取至少两个目标子簇；针对每个点云簇，确定目标子簇的数据点数量间的比值；若比值中存在满足比值条件的目标比值，则确定激光点云帧中包含标志物，并对机器人进行运动控制。
其中，目标子簇为全部子簇中满足选取条件的子簇。例如，选取条件可以是数据点数 量大于预设值。又例如，选取条件可以是数据点数量在所有子簇中的排序在预设名次内，预设名次例如可以是2。
比值条件是根据比值判断点云簇是否为标志物的条件。在一个实施例中，比值条件可以为比值小于预设值，例如预设值可以为0.5、0.6等。在另一个实施例中，比值条件可以为比值在预设比值区间内。机器人可以根据图形组合中各图形元素的大小确定预设比值区间。例如，若图形组合中包括两个图形元素，两个图形元素的大小相同，图形元素的大小间的比值为1，则当目标子簇的数据点数量间的比值远大于1或者远小于1时，说明各目标子簇的大小相差较大，不符合图形组合中各图形元素的大小特征，不是标志物对应的点云簇。因此，机器人可以确定预设比值区间为[0.5，1.5]。若比值中存在满足比值条件的目标比值，说明点云簇中各目标子簇符合图形组合中各图形元素的大小特征，从而确定该点云簇为标志物。
上述实施例中，在每个点云簇的子簇中选取至少两个目标子簇；针对每个点云簇，确定目标子簇的数据点数量间的比值；若比值中存在满足比值条件的目标比值，则确定激光点云帧中包含标志物，并对机器人进行运动控制。根据点云簇中各目标子簇的大小是否符合图形组合中各图形元素的大小特征，判断点云簇是否为标志物，进一步提高了识别标志物的准确性。
在一个实施例中，如图7所示，机器人运动控制的方法包括如下步骤：
S702，获取对运行环境进行扫描所得的激光点云帧。
其中，激光点云帧中各数据点对应有反射特征，该反射特征可以是反射强度或结构特征。当该反射特征为反射强度时，执行S704。
S704，根据激光点云帧中各数据点对应的反射强度，确定数据点的平均反射强度与最大反射强度。
S706，根据平均反射强度与最大反射强度确定强度阈值，并基于强度阈值，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧。
S708，当标志物是由至少两个图形元素组成的图形组合时，根据图形组合中各图形元素间的距离确定第一聚类阈值和第二聚类阈值；第一聚类阈值小于第二聚类阈值。
S710，根据第一聚类阈值，对过滤后的激光点云帧中的数据点进行聚类，并在聚类所得的类簇中选取满足第一形状条件的第一类簇；第一形状条件是基于图形元素的形状确定的。
S712，根据第二聚类阈值，对第一类簇中的数据点进行聚类，并在聚类所得的类簇中选取满足第二形状条件的第二类簇；第二形状条件是基于图形组合的形状确定的。
S714，根据第一聚类阈值，将每个第二类簇聚类为多个子簇，并确定每个第二类簇中子簇的数量。
S716，基于第二类簇中子簇的数量对第二类簇进行过滤，得到点云簇，并在每个点云簇的子簇中选取至少两个目标子簇。
S718，针对每个点云簇，确定目标子簇的数据点数量间的比值。
S720，若比值中存在满足比值条件的目标比值，则确定激光点云帧中包含标志物，并确定机器人与标志物间的距离；根据距离对机器人进行运动控制。
上述S702至S720的具体内容可以参考上文所述的具体实现过程。
应该理解的是，虽然如上所述的各实施例所涉及的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，如上所述的各实施例所涉及的流程图中的至少一部分步骤可以包括多个步骤或者多个阶段，这些步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。
基于同样的发明构思，本申请实施例还提供了一种用于实现上述所涉及的机器人运动控制的方法对应的机器人运动控制的装置。该装置所提供的解决问题的实现方案与上述方法中所记载的实现方案相似，故下面所提供的一个或多个机器人运动控制的装置实施例中的具体限定可以参见上文中对于机器人运动控制的方法的限定，在此不再赘述。
在一个实施例中，如图8所示，提供了一种机器人运动控制的装置，包括：获取模块802、过滤模块804、聚类模块806和控制模块808，其中：
获取模块802，用于获取对运行环境进行扫描所得的激光点云帧；
过滤模块804，用于根据激光点云帧中各数据点对应的反射特征，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧；
聚类模块806，用于对过滤后的激光点云帧中的数据点进行聚类，得到点云簇；
控制模块808，用于若基于点云簇确定激光点云帧中包含标志物，则对机器人进行运动控制。
上述实施例中，获取对运行环境进行扫描所得的激光点云帧；由于标志物对激光的反射特征与运行环境中的其它物体的反射特征不同，因此根据激光点云帧中各数据点对应的反射特征，对激光点云帧中的数据点进行过滤，从而可以将反射特征不同于其它物体的数据点过滤出来，得到过滤后的激光点云帧。对过滤后的激光点云帧中的数据点进行聚类，得到点云簇，从而可以根据标志物的形状特征判断激光点云帧中是否包含标志物。若基于点云簇确定激光点云帧中包含标志物，则对机器人进行运动控制。通过利用标志物对激光反射不同的特征以及形状特征在激光点云帧中识别标志物，识别精度更高，有效避免了机器人进入危险区域和禁止区域。并且激光的感应距离较远，在较远处即可识别标志物，使机器人有充足的时间进行刹车，有效避免了快速移动的机器人跌落或者因紧急刹车而倾倒，保障了机器人的安全运行。
在一个实施例中，控制模块808，还用于：
若基于点云簇确定激光点云帧中包含标志物，则确定机器人与标志物对应间的距离；
根据距离对机器人进行运动控制。
在一个实施例中，激光点云帧包括至少两帧；控制模块808，还用于：
若基于点云簇确定激光点云帧中包含标志物，则分别确定各标志物对应的位置坐标；
若至少两帧激光点云帧中的标志物对应的位置坐标相同，则对机器人进行运动控制。
在一个实施例中，过滤模块804，还用于：
根据激光点云帧中各数据点对应的反射强度，确定数据点的平均反射强度与最大反射强度；
根据平均反射强度与最大反射强度确定强度阈值；
基于强度阈值，对激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧。
在一个实施例中，标志物为由至少两个图形元素组成的图形组合；聚类模块806，还用于：
根据图形组合中各图形元素间的距离确定第一聚类阈值和第二聚类阈值；第一聚类阈值小于第二聚类阈值；
根据第一聚类阈值，对过滤后的激光点云帧中的数据点进行聚类，并在聚类所得的类簇中选取满足第一形状条件的第一类簇；第一形状条件是基于图形元素的形状确定的；
根据第二聚类阈值，对第一类簇中的数据点进行聚类，并在聚类所得的类簇中选取满足第二形状条件的第二类簇；第二形状条件是基于图形组合的形状确定的；
对第二类簇进行过滤，得到点云簇。
在一个实施例中，聚类模块806，还用于：
根据第一聚类阈值，将每个第二类簇聚类为多个子簇，并确定每个第二类簇中子簇的数量；
基于子簇的数量对第二类簇进行过滤，得到点云簇。
在一个实施例中，控制模块808，还用于：
在每个点云簇的子簇中选取至少两个目标子簇；
针对每个点云簇，确定目标子簇的数据点数量间的比值；
若比值中存在满足比值条件的目标比值，则确定激光点云帧中包含标志物，并对机器人进行运动控制。
上述机器人运动控制的装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
在一个实施例中，提供了一种机器人，其内部结构图可以如图9所示。该机器人包括处理器、存储器、输入/输出接口、通信接口、显示单元和输入装置。其中，处理器、存储器和输入/输出接口通过系统总线连接，通信接口、显示单元和输入装置通过输入/输出接口连接到系统总线。其中，该机器人的处理器用于提供计算和控制能力。该机器人的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程 序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该机器人的输入/输出接口用于处理器与外部设备之间交换信息。该机器人的通信接口用于与外部的终端进行有线或无线方式的通信，无线方式可通过WIFI、移动蜂窝网络、NFC(近场通信)或其他技术实现。该计算机程序被处理器执行时以实现一种机器人运动控制的方法。该机器人的显示单元用于形成视觉可见的画面，可以是显示屏、投影装置或虚拟现实成像装置，显示屏可以是液晶显示屏或电子墨水显示屏，该机器人的输入装置可以是显示屏上覆盖的触摸层，也可以是机器人外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图9中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的机器人可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种机器人，包括存储器和处理器，存储器中存储有计算机程序，所述机器人上搭载有激光雷达，所述机器人的运行环境中设置有标志物，该处理器执行计算机程序时实现上述各方法实施例中的步骤。
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现上述各方法实施例中的步骤。
在一个实施例中，提供了一种计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实现上述各方法实施例中的步骤。
需要说明的是，本申请所涉及的用户信息(包括但不限于用户设备信息、用户个人信息等)和数据(包括但不限于用于分析的数据、存储的数据、展示的数据等)，均为经用户授权或者经过各方充分授权的信息和数据，且相关数据的收集、使用和处理需要遵守相关国家和地区的相关法律法规和标准。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、数据库或其它介质的任何引用，均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-Only Memory，ROM)、磁带、软盘、闪存、光存储器、高密度嵌入式非易失性存储器、阻变存储器(ReRAM)、磁变存储器(Magnetoresistive Random Access Memory，MRAM)、铁电存储器(Ferroelectric Random Access Memory，FRAM)、相变存储器(Phase Change Memory，PCM)、石墨烯存储器等。易失性存储器可包括随机存取存储器(Random Access Memory，RAM)或外部高速缓冲存储器等。作为说明而非局限，RAM可以是多种形式，比如静态随机存取存储器(Static Random Access Memory，SRAM)或动态随机存取存储器(Dynamic Random Access Memory，DRAM)等。本申请所提供的各实施例中所涉及的数据库可包括关系型数据库和非关系型数据库中至少一种。非关系型数据库可包括基于区块链的分布式 数据库等，不限于此。本申请所提供的各实施例中所涉及的处理器可为通用处理器、中央处理器、图形处理器、数字信号处理器、可编程逻辑器、基于量子计算的数据处理逻辑器等，不限于此。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对申请专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种机器人运动控制的方法，所述机器人上搭载有激光雷达，所述机器人的运行环境中设置有标志物，所述方法包括：
    获取对运行环境进行扫描所得的激光点云帧；
    根据所述激光点云帧中各数据点对应的反射特征，对所述激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧；
    对所述过滤后的激光点云帧中的数据点进行聚类，得到点云簇；及
    若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制。
  2. 根据权利要求1所述的方法，其特征在于，所述若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制包括：
    若基于所述点云簇确定所述激光点云帧中包含标志物，则确定所述机器人与所述标志物间的距离；及
    根据所述距离对所述机器人进行运动控制。
  3. 根据权利要求1所述的方法，其特征在于，所述激光点云帧包括至少两帧；所述若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制包括：
    若基于所述点云簇确定所述激光点云帧中包含标志物，则分别确定各所述标志物对应的位置坐标；及
    若至少两帧所述激光点云帧中的标志物对应的位置坐标相同，则对所述机器人进行运动控制。
  4. 根据权利要求1所述的方法，其特征在于，所述标志物包括反光标志物，所述反射特征包括反射强度；所述根据所述激光点云帧中各数据点对应的反射特征，对所述激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧包括：
    根据所述激光点云帧中各数据点对应的反射强度确定强度阈值；及
    在所述激光点云帧的数据点中，过滤出反射强度大于所述强度阈值的数据点，得到过滤后的激光点云帧。
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述激光点云帧中各数据点对应的反射强度确定强度阈值包括：
    根据所述激光点云帧中各数据点对应的反射强度，确定所述数据点的平均反射强度与最大反射强度；及
    根据所述平均反射强度与所述最大反射强度确定强度阈值。
  6. 根据权利要求1所述的方法，其特征在于，所述标志物为由至少两个图形元素组成的图形组合；所述对所述过滤后的激光点云帧中的数据点进行聚类，得到点云簇包括：
    根据所述图形组合中各图形元素间的距离确定第一聚类阈值和第二聚类阈值；所述第 一聚类阈值小于所述第二聚类阈值；
    根据所述第一聚类阈值，对所述过滤后的激光点云帧中的数据点进行聚类，并在聚类所得的类簇中选取满足第一形状条件的第一类簇；所述第一形状条件是基于所述图形元素的形状确定的；
    根据所述第二聚类阈值，对所述第一类簇中的数据点进行聚类，并在聚类所得的类簇中选取满足第二形状条件的第二类簇；所述第二形状条件是基于所述图形组合的形状确定的；及
    对所述第二类簇进行过滤，得到所述点云簇。
  7. 根据权利要求6所述的方法，其特征在于，所述对所述第二类簇进行过滤，得到点云簇包括：
    根据所述第一聚类阈值，将每个所述第二类簇聚类为多个子簇，并确定每个所述第二类簇中子簇的数量；及
    基于所述第二类簇中子簇的数量对所述第二类簇进行过滤，得到所述点云簇。
  8. 根据权利要求1至7任一项所述的方法，其特征在于，所述若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制包括：
    在每个所述点云簇的子簇中选取至少两个目标子簇；
    针对每个所述点云簇，确定所述目标子簇的数据点数量间的比值；及
    若所述比值中存在满足比值条件的目标比值，则确定所述激光点云帧中包含标志物，并对所述机器人进行运动控制。
  9. 根据权利要求1至8任一项所述的方法，其特征在于，所述方法还包括：
    当所述反射特征为结构特征时，根据所述点云簇判断所述激光点云帧中是否包含结构化标志物，
    所述若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制包括：
    若基于所述点云簇确定所述激光点云帧中包含结构化标志物，则对所述机器人进行运动控制。
  10. 根据权利要求1至9任一项所述的方法，其特征在于，所述对所述机器人进行运动控制包括：
    控制所述机器人停止移动；或者，
    控制所述机器人绕过危险区域；或者，
    控制所述机器人降低移动速度。
  11. 一种机器人运动控制的装置，其特征在于，所述装置包括：
    获取模块，用于获取对运行环境进行扫描所得的激光点云帧；
    过滤模块，用于根据所述激光点云帧中各数据点对应的反射特征，对所述激光点云帧中的数据点进行过滤，得到过滤后的激光点云帧；
    聚类模块，用于对所述过滤后的激光点云帧中的数据点进行聚类，得到点云簇；
    控制模块，用于若基于所述点云簇确定所述激光点云帧中包含标志物，则对所述机器人进行运动控制。
  12. 根据权利要求11所述的装置，其特征在于，所述控制模块，还用于若基于所述点云簇确定所述激光点云帧中包含标志物，则确定所述机器人与所述标志物间的距离；及根据所述距离对所述机器人进行运动控制。
  13. 根据权利要求11所述的装置，其特征在于，所述控制模块，还用于若基于所述点云簇确定所述激光点云帧中包含标志物，则分别确定各所述标志物对应的位置坐标；及若至少两帧所述激光点云帧中的标志物对应的位置坐标相同，则对所述机器人进行运动控制。
  14. 根据权利要求11所述的装置，其特征在于，所述标志物包括反光标志物，所述反射特征包括反射强度；
    所述过滤模块，还用于根据所述激光点云帧中各数据点对应的反射强度确定强度阈值；及在所述激光点云帧的数据点中，过滤出反射强度大于所述强度阈值的数据点，得到过滤后的激光点云帧。
  15. 根据权利要求14所述的装置，其特征在于，所述根据所述激光点云帧中各数据点对应的反射强度确定强度阈值包括：
    根据所述激光点云帧中各数据点对应的反射强度，确定所述数据点的平均反射强度与最大反射强度；及
    根据所述平均反射强度与所述最大反射强度确定强度阈值。
  16. 根据权利要求11所述的装置，其特征在于，所述标志物为由至少两个图形元素组成的图形组合；
    所述聚类模块，还用于根据所述图形组合中各图形元素间的距离确定第一聚类阈值和第二聚类阈值；所述第一聚类阈值小于所述第二聚类阈值；根据所述第一聚类阈值，对所述过滤后的激光点云帧中的数据点进行聚类，并在聚类所得的类簇中选取满足第一形状条件的第一类簇；所述第一形状条件是基于所述图形元素的形状确定的；根据所述第二聚类阈值，对所述第一类簇中的数据点进行聚类，并在聚类所得的类簇中选取满足第二形状条件的第二类簇；所述第二形状条件是基于所述图形组合的形状确定的；及对所述第二类簇进行过滤，得到所述点云簇。
  17. 根据权利要求16所述的装置，其特征在于，所述聚类模块，还用于根据所述第一聚类阈值，将每个所述第二类簇聚类为多个子簇，并确定每个所述第二类簇中子簇的数量；及基于所述第二类簇中子簇的数量对所述第二类簇进行过滤，得到所述点云簇。
  18. 根据权利要求11至17任一项所述的装置，其特征在于，所述控制模块，还用于在每个所述点云簇的子簇中选取至少两个目标子簇；针对每个所述点云簇，确定所述目标子簇的数据点数量间的比值；及若所述比值中存在满足比值条件的目标比值，则确定所述 激光点云帧中包含标志物，并对所述机器人进行运动控制。
  19. 一种机器人，包括存储器和处理器，所述存储器存储有计算机程序，所述机器人上搭载有激光雷达，所述机器人的运行环境中设置有标志物，所述处理器执行所述计算机程序时实现权利要求1至10中任一项所述的方法的步骤。
  20. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至10中任一项所述的方法的步骤。
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