WO2022067534A1 - Procédé et dispositif de génération de carte de grille d'occupation - Google Patents

Procédé et dispositif de génération de carte de grille d'occupation Download PDF

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Publication number
WO2022067534A1
WO2022067534A1 PCT/CN2020/118950 CN2020118950W WO2022067534A1 WO 2022067534 A1 WO2022067534 A1 WO 2022067534A1 CN 2020118950 W CN2020118950 W CN 2020118950W WO 2022067534 A1 WO2022067534 A1 WO 2022067534A1
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WIPO (PCT)
Prior art keywords
point cloud
road surface
curved
ground
occupied
Prior art date
Application number
PCT/CN2020/118950
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English (en)
Chinese (zh)
Inventor
孙翔雨
Original Assignee
华为技术有限公司
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Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to PCT/CN2020/118950 priority Critical patent/WO2022067534A1/fr
Priority to CN202080004371.0A priority patent/CN112543938B/zh
Publication of WO2022067534A1 publication Critical patent/WO2022067534A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Definitions

  • the present application relates to computer technology, and in particular, to a method and apparatus for generating an occupancy grid map.
  • the occupancy grid map (OGM) in the scale map is the most widely used.
  • the detection area can be divided into grids of a certain number and size, and the probability of each grid being occupied can be determined according to the detection results of the detector, and the occupied probability of each grid can be reflected to the corresponding detection area.
  • the occupancy grid map can be obtained, in which the detector can be a point cloud sensor. Therefore, the occupancy grid map can reflect the obstacle information in the detection area.
  • the current method for obtaining the occupancy grid map is not accurate enough to accurately reflect the obstacle information in the detection area.
  • the present application provides a method and device for generating an occupancy grid map, which can obtain an accurate occupancy grid map.
  • an embodiment of the present application provides a method for generating an occupancy grid map, including: acquiring a point cloud of a surrounding environment collected by a point cloud sensor; acquiring an obstacle point cloud and a ground surface from the point cloud of the surrounding environment point cloud; according to the ground point cloud and the characteristics of the ground, fit a curved driving road surface, the ground is the ground of the road where the mobile platform is located, and the point cloud sensor is mounted on the mobile platform; driving from the curved surface
  • the road surface area to be driven is determined in the road surface, and the road surface area to be driven is divided into a plurality of grids; according to the obstacle point cloud, the probability of each grid being occupied is determined; according to the occupied probability of each grid Probability and the area of the road to be traveled, generate a surface occupancy grid map.
  • the curved road surface is fitted based on the characteristics of the ground, and the road surface area to be driven is determined from the curved road surface.
  • the occupancy probability of each grid in the obtained road surface area is Accuracy increases, increasing the accuracy of the resulting occupancy raster map.
  • fitting the curved road surface includes: acquiring the centerline of the road where the ground is located; The vertical distance of the center line is less than or equal to the first point of the preset distance; using polynomial curve interpolation to fit each of the first points to obtain the road center fitting curve; fitting the curve according to the road center to obtain the curved road surface, The road center fitting curve is the center line of the curved road surface.
  • This scheme presents a specific realization of fitting curved road surface.
  • the determining the probability that each of the occupied grids is occupied according to the obstacle point cloud includes: extracting the distance between the obstacle point cloud and the curved road surface Each second point whose height difference is less than or equal to the maximum height of the vehicle; according to each of the second points, determine the probability that each of the occupied grids is occupied.
  • This solution can avoid misjudging the hanging objects on the road surface or the tops of bridge holes or tunnels as obstacles, and improves the accuracy of determining the probability that each grid in the road surface area to be driven is occupied, thereby improving the generated occupancy grid. accuracy of the grid map.
  • the determining the probability that each of the occupied grids is occupied according to the second point includes: for any one of the second points, determining the second point The first grid occupied by the point, and the influence probability of the second point on the first grid; add the influence probability of the second point occupying the same grid on the grid to obtain each of the grids The preselected occupancy probability of the grid; for any grid in each of the grids, the occupancy probability of the grid is obtained according to the first preselected occupancy probability of the grid and the occupancy probability of the grid at the previous moment.
  • determining the area of the road to be driven from the curved road surface includes: determining the area to be driven from the curved road surface according to the field of view of the point cloud sensor and the curved road surface. driving road area.
  • the determining the area of the road to be driven from the curved road surface according to the field of view of the point cloud sensor and the curved road surface includes: according to the field of view of the point cloud sensor and the curved road surface , determine a first length, the first length is less than or equal to the length of the curved road surface; from the curved road surface, the length is determined as the first length, and the width is the first width of the road surface area to be driven , the first width is the width of the curved road surface.
  • the determining the area of the road to be driven from the curved road surface according to the field of view of the point cloud sensor and the curved road surface includes: according to the field of view of the point cloud sensor and the curved road surface , determine a first length and a second width, the first length is less than or equal to the length of the curved driving surface, and the second width is less than or equal to the width of the curved driving surface; determine from the curved driving surface The length is the first length, and the width is the to-be-running road surface area of the second width.
  • one side of the road surface area to be driven is coincident with the side of the curved driving surface area close to the mobile platform. This scheme determines the road area to be driven more reasonably and accurately.
  • an embodiment of the present application provides an apparatus for generating an occupancy grid map, including: an acquisition module for acquiring a point cloud of the surrounding environment collected by a point cloud sensor; and a processing module for: from the surrounding environment Obtain the obstacle point cloud and the ground point cloud from the point cloud of the mobile platform; and fit the curved driving road surface according to the characteristics of the ground point cloud and the ground, the ground is the ground of the road where the mobile platform is located, and the point cloud sensor is equipped with on the mobile platform; and determining a road surface area to travel from the curved road surface, the road surface area to travel is divided into a plurality of grids; and determining each grid according to the obstacle point cloud occupied probability; and generating a surface occupied grid map according to the occupied probability of each grid and the to-be-traveled road surface area.
  • the processing module is specifically configured to: acquire the centerline of the road where the ground is located; acquire the first point cloud whose vertical distance from the centerline is less than or equal to a preset distance One point; use polynomial curve interpolation to fit each of the first points to obtain the road center fitting curve; according to the road center fitting curve, obtain the curved driving surface, and the road center fitting curve is the curved driving road surface the centerline.
  • the processing module is specifically configured to: extract each second point whose height difference between the obstacle point cloud and the curved road surface is less than or equal to the maximum height of the vehicle; According to each of the second points, the probability that each of the occupied grids is occupied is determined.
  • the processing module is specifically configured to: determine the road surface area to be driven from the curved driving road surface according to the field of view of the point cloud sensor and the curved driving road surface.
  • the processing module is specifically configured to: determine a first length according to the field of view of the point cloud sensor and the curved road surface, where the first length is less than or equal to the curved surface The length of the driving road surface; the length of the curved driving road surface is determined as the first length and the width is the first width of the road surface area to be driven, and the first width is the width of the curved driving road surface.
  • the processing module is specifically configured to: determine a first length and a second width according to the field of view of the point cloud sensor and the curved road surface, where the first length is less than or is equal to the length of the curved driving surface, and the second width is less than or equal to the width of the curved driving surface; the length determined from the curved driving surface is the first length, and the width is all the second width. Describe the road area to be driven.
  • one side of the road surface area to be driven is coincident with the side of the curved driving surface area close to the mobile platform.
  • embodiments of the present application provide a point cloud sensor, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores data that can be used by the at least one processor Instructions to be executed, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the first aspect or any possible implementation of the first aspect.
  • an embodiment of the present application provides a mobile platform on which the point cloud sensor described in the third aspect is mounted.
  • an embodiment of the present application provides a mobile platform, including a point cloud sensor and a processor; the point cloud sensor is configured to collect a point cloud of a surrounding environment, and send the point cloud of the surrounding environment to the processing the processor; the processor is configured to receive the point cloud of the surrounding environment, obtain the obstacle point cloud and the ground point cloud from the point cloud of the surrounding environment; and, according to the characteristics of the ground point cloud and the ground, fit a curved driving road surface, the ground is the ground of the road where the mobile platform is located, and the point cloud sensor is mounted on the mobile platform; is divided into a plurality of grids; and, according to the obstacle point cloud, determine the probability that each grid is occupied; and, according to the probability that each grid is occupied and the road area to be driven, generate The surface occupies the raster map.
  • the processor is further configured to execute the method described in any possible implementation manner of the first aspect.
  • an embodiment of the present application provides a storage medium, wherein the storage medium includes a computer program, and the computer program is used to implement the first aspect or any possible implementation manner of the first aspect. method.
  • FIG. 1 is a schematic diagram of a current plane occupying a grid map
  • FIG. 2 is a schematic diagram 1 of a scene for obtaining an occupied grid map of a road surface provided by an embodiment of the present application;
  • FIG. 3 is a schematic diagram 2 of a scene for obtaining a grid map of road occupancy provided by an embodiment of the present application;
  • FIG. 4 is a schematic diagram 3 of a scenario for obtaining a grid map of road occupancy provided by an embodiment of the present application
  • FIG. 5 is a schematic diagram 4 of a scenario for acquiring a grid map of road occupancy provided by an embodiment of the present application
  • FIG. 6 is a schematic diagram 5 of a scenario for obtaining an occupied grid map of a road surface provided by an embodiment of the present application;
  • FIG. 7 is a schematic diagram 6 of a scenario for obtaining an occupied grid map of a road surface provided by an embodiment of the present application.
  • FIG. 8 is a flowchart of a method for generating an occupancy grid map provided by an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a road centerline provided by an embodiment of the present application.
  • FIG. 10 is a schematic diagram of a road surface area to be driven according to an embodiment of the application.
  • FIG. 11 is a schematic diagram of a second point in an obstacle point cloud provided by an embodiment of the present application.
  • FIG. 12 is a schematic diagram of a curved surface occupying a grid map provided by an embodiment of the present application.
  • FIG. 13 is a schematic block diagram of an apparatus for generating an occupancy grid map provided by an embodiment of the present application.
  • FIG. 14 is a schematic block diagram of a mobile platform provided by an embodiment of the present application.
  • FIG. 15 is a schematic block diagram of an electronic device according to an embodiment of the present application.
  • the flat occupancy grid map is based on the assumption that the detection area is a plane, and divides the plane detection area into multiple grids.
  • the detector such as radar and camera
  • a schematic diagram of a plane occupied grid map can be shown in Figure 1. The darker the color filled in the grid, the greater the probability that the grid is occupied.
  • Curved occupancy grid map refers to dividing the surface detection area into multiple grids. According to the detection data of the surrounding environment by the detector, the probability that each grid is occupied by obstacles is determined. The probability that each grid is occupied by obstacles is reflected to the corresponding grid in the surface detection area, and the grid map of surface occupancy is obtained.
  • Figures 2 to 7 are schematic diagrams of several scenarios for obtaining the occupancy grid map of the road surface.
  • the vehicles in Figures 2 to 7 are equipped with detectors.
  • the road in Figure 2 is a flat road.
  • the road in Fig. 3 is a curved road with an upward slope
  • the road in Fig. 4 is a curved road with a downward slope
  • the road in Fig. 5 is a curved road with unevenness
  • the road is a road with bridge holes and tunnels.
  • the road is assumed to be a plane road, and the plane road is fitted based on the ground point cloud obtained by the detector, and a drivable area is determined from the plane road according to the field of view of the detector.
  • the drivable area is divided into multiple grids, and the probability that each grid is occupied by obstacles is determined according to the obstacle point cloud obtained by the detector, and the probability that each grid is occupied by obstacles is reflected to the corresponding drivable area. on the grid to get the plane occupancy grid map.
  • the curved road surface is fitted based on the actual characteristics of the ground, which can improve the accuracy of the obtained occupancy grid map.
  • FIG. 8 illustrates a method for generating an occupancy grid map provided by the implementation of the present application.
  • the execution subject of this embodiment may be a generating device for occupying a grid map.
  • the method of this embodiment includes:
  • Step S801 acquiring the point cloud of the surrounding environment collected by the point cloud sensor.
  • the point cloud sensor in this embodiment may be a time of flight (TOF) sensor, or a radar or a camera.
  • the radar can be a lidar, and the lidar can be a rotating lidar or a solid-state lidar.
  • the point cloud sensor can be mounted on the mobile platform to obtain the point cloud of the surrounding environment of the mobile platform.
  • the mobile platform can be a vehicle, such as an autonomous vehicle.
  • the device for generating the occupancy grid map in this embodiment may be all or part of the point cloud sensor, or all or part of the mobile platform equipped with the point cloud sensor, or may be connected to the point cloud sensor or the mobile platform for communication All or part of the server or end device of the relationship.
  • Step S802 Obtain the obstacle point cloud and the ground point cloud from the point cloud of the surrounding environment.
  • the point cloud of the surrounding environment includes the obstacle point cloud and the ground point cloud.
  • the ground point cloud can be extracted from the point cloud of the surrounding environment first, and the remaining point cloud is the obstacle point cloud.
  • the ground point cloud fast segmentation algorithm can be used to extract the ground point cloud from the point cloud of the surrounding environment.
  • the method of this embodiment can accurately extract the ground point cloud from the point cloud of the surrounding environment. For example, in the scene shown in FIG. 3 , the method of this embodiment to extract the ground point cloud from the point cloud of the surrounding environment The point cloud corresponding to the uphill will not be misjudged as the obstacle point cloud.
  • Step S803 Fitting a curved driving road surface according to the ground point cloud and the characteristics of the ground.
  • the ground is the ground of the road where the mobile platform is located, and the point cloud sensor is mounted on the mobile platform.
  • the surface driving road can be fitted according to the ground point cloud.
  • the curved road surface is fitted, including the following a1 to a4:
  • the road in this embodiment is a road traveled by a mobile platform equipped with a point cloud sensor, wherein the centerline of the road where the ground is located may indicate the characteristics of the ground.
  • the range of the centerline can be calculated according to the boundary in the width direction of the road, and then the equation of the centerline of the road where the ground is located can be obtained, which is used to indicate the centerline of the road where the ground is located.
  • the center line of the road where the ground is located is parallel to the extending direction of the road.
  • 901 shown in FIG. 9 is the center line of the road where the ground is located.
  • acquiring the first point in the ground point cloud whose vertical distance from the center line is less than or equal to a preset distance includes: filtering the ground point cloud, obtaining a filtered ground point cloud, and determining the filtered ground point The point in the cloud whose vertical distance from the center line is less than or equal to the preset distance is the first point.
  • polynomial curve interpolation is used to fit each first point to obtain a road center fitting curve.
  • the curved road surface is obtained, and the road center fitting curve is the center line of the curved road surface.
  • moving a line segment with the same width of the road along the center fitting curve can form a straight surface, that is, a curved road surface.
  • the first end of the fitted curve through the center of the road, the width of the road on which the mobile platform where the point cloud sensor is located, and the first line segment perpendicular to the extending direction of the road can be obtained, and the first line segment can be obtained.
  • the obtained surface with the road center fitting curve as the center line is the curved driving surface. It can be understood that the first line segment is always perpendicular to the extending direction of the road during the moving process.
  • the curved road surface is equivalent to a curved surface obtained by moving the first line segment from the first end of the road center fitting curve to the second end of the road center fitting curve along the road center fitting curve.
  • Step S804 Determine the road surface area to be driven from the curved road surface, and the road surface area to be driven is divided into a plurality of grids.
  • the side of the road surface area to be driven close to the mobile platform can be overlapped with the side of the curved road surface close to the mobile platform, and the point cloud sensor is mounted on the mobile platform.
  • the road surface area to be driven is determined from the curved road surface, which may specifically include the following b1 to b2:
  • the preset length may be stored in the generating device occupying the grid map.
  • the area of the road surface to be driven determined in this specific implementation is an area in the curved road surface where the length is the preset length and the width is the first width.
  • the road surface area to be traveled can be abstracted as a plane formed by a straight line perpendicular to the advancing direction of the movable platform along the arc curve in the center of the road from moving a preset length.
  • the road surface area to be driven is divided into a plurality of grids of the same size, such as M ⁇ N grids of the same size, wherein M and N are both positive integers.
  • the length of the road surface area to be driven is a preset length, the efficiency of determining the road surface area to be driven is relatively high.
  • the area to be driven on the road surface may be determined from the curved road surface according to the detection range of the sensor and the curved road surface, which may specifically include the following c1 to c2:
  • the first length is the farthest distance that the point cloud sensor can detect, and when the farthest distance that the point cloud sensor can detect is greater than or When equal to the length of the curved road surface, the first length is equal to the length of the curved road surface.
  • the road surface area to be driven is an area of the curved road surface where the length is the first length and the width is the first width.
  • the road surface area to be driven is divided into a plurality of grids of the same size, as shown in FIG. 10 .
  • the road area to be driven in FIG. 10 may be the road area to be driven obtained in the scenario shown in FIG. 3 . It can be understood that there are grids of different sizes in the road area to be driven because the grids are visually different.
  • the driving road surface is fitted to a curved surface. In fact, the size of each grid included in the road surface area to be driven is the same.
  • the determined road surface area to be driven on is more reasonable and accurate.
  • the area to be driven on the road surface may be determined from the curved road surface according to the detection range of the sensor, which may specifically include the following d1 to d2:
  • the first length is less than or equal to the length of the curved road surface
  • the second width is less than or equal to the width of the curved road surface.
  • the method for determining the first length is the same as above, and details are not repeated here.
  • the second width when the maximum width that can be detected by the point cloud sensor is smaller than the first width of the curved road surface, the second width is the maximum width that the point cloud sensor can detect. When greater than or equal to the first width, the second width is equal to the first width.
  • the determined road surface area to be driven on is more reasonable and accurate.
  • Step S805 Determine the probability that each grid is occupied according to the obstacle point cloud.
  • the method for determining the probability that each grid in the road surface area to be driven is occupied according to the obstacle point cloud may refer to the current general method, which will not be repeated here.
  • each The probability that the grid is occupied can include the following e1 ⁇ e2:
  • the vertical height difference between each second point and the curved road surface is less than or equal to the maximum height of the vehicle.
  • a, b, c are constants, m is an integer greater than or equal to 2, n is a positive integer, such as 1 or 2 or 3, and k is a positive integer, such as 1 or 2 or 3.
  • 111 is a side view of a curved driving road surface, and the points between the curve 112 and the curve 111 are the second points.
  • each second point determine the probability that each grid in the road surface area to be driven is occupied.
  • the method for determining the influence probability of the second point on the first grid may refer to the current general method, which will not be repeated here.
  • the initial occupancy probability of each grid is 0.
  • the method for obtaining the occupancy probability of the first grid may refer to the current general method, which will not be repeated here.
  • the method e1-e2 determines the probability that each grid in the road area to be driven is occupied can avoid misjudging the hanging objects on the road surface or the top of a bridge or tunnel as an obstacle, which improves the determination of the probability of being occupied in the road area to be driven.
  • Step S806 according to the occupied probability of each grid in the road surface area to be driven and the road surface area to be driven, generate a grid map occupied by the curved surface.
  • the surface occupied grid map can be generated. Specifically, as shown in Figure 12, the darker the color, the greater the probability that the grid is occupied.
  • the curved road surface is fitted based on the characteristics of the ground, and the road surface area to be driven is determined from the curved road surface. area, and generate a surface occupied grid map, that is, the ground point cloud is no longer flatly fitted, but the surface is fitted to obtain a curved driving road surface that conforms to the actual characteristics of the ground, then the occupancy probability of each grid in the road area to be driven is obtained. will increase the accuracy of the resulting occupancy raster map.
  • FIG. 13 is a schematic block diagram of an apparatus for generating an occupancy grid map provided by an embodiment of the present application.
  • the apparatus in this embodiment includes an acquisition module 1301 and a processing module 1302 .
  • the acquisition module 1301 is used to acquire the point cloud of the surrounding environment collected by the point cloud sensor;
  • the processing module 1302 is used for:
  • the ground is the ground of the road where the mobile platform is located, and the point cloud sensor is mounted on the mobile platform;
  • the road surface area to travel is divided into a plurality of grids
  • a grid map occupied by a curved surface is generated.
  • processing module 1302 is specifically used for:
  • the curved driving surface is obtained, and the road center fitting curve is the center line of the curved driving road surface.
  • processing module 1302 is specifically used for:
  • the probability that each of the occupied grids is occupied is determined.
  • processing module 1302 is specifically used for:
  • a road surface area to be driven is determined from the curved road surface.
  • processing module 1302 is specifically used for:
  • the length of the road surface area to be driven is determined as the first length and the width is the first width, and the first width is the width of the curved driving surface.
  • processing module 1302 is specifically used for:
  • a first length and a second width are determined, the first length is less than or equal to the length of the curved road surface, and the second width is less than or equal to the the width of the surface driving surface;
  • a length of the first length and a width of the to-be-run surface area of the second width are determined from the curved running surface.
  • one side of the road surface area to be driven is coincident with the side of the curved road surface area close to the mobile platform.
  • the apparatus in this embodiment can be used to execute the technical solutions in the foregoing method embodiments, and the implementation principles and technical effects thereof are similar, and details are not described herein again.
  • FIG. 14 is a schematic block diagram of a mobile platform provided by an embodiment of the present application.
  • the apparatus of this embodiment includes: a point cloud sensor 1401 and a processor 1402;
  • the point cloud sensor is used for collecting the point cloud of the surrounding environment, and sending the point cloud of the surrounding environment to the processor;
  • the processor is configured to receive the point cloud of the surrounding environment, obtain the obstacle point cloud and the ground point cloud from the point cloud of the surrounding environment; and, according to the characteristics of the ground point cloud and the ground, fit a curved surface for driving a road surface, where the ground is the ground of the road where the mobile platform is located, and the point cloud sensor is mounted on the mobile platform; and a road surface area to be driven is determined from the curved road surface, and the road surface area to be driven is divided is a plurality of grids; and, according to the obstacle point cloud, determine the probability that each grid is occupied; and, according to the probability that each grid is occupied and the road area to be driven, generate a curved surface occupied Raster map.
  • the processor 1402 is specifically configured to:
  • the curved driving surface is obtained, and the road center fitting curve is the center line of the curved driving road surface.
  • the processor 1402 is specifically configured to:
  • the probability that each of the occupied grids is occupied is determined.
  • the processor 1402 is specifically configured to: determine the road surface area to be driven from the curved driving road surface according to the field of view of the point cloud sensor and the curved driving road surface.
  • processor 1402 the processor 1402:
  • the length of the road surface area to be driven is determined as the first length and the width is the first width, and the first width is the width of the curved driving surface.
  • the processor 1402 is specifically configured to:
  • a first length and a second width are determined, the first length is less than or equal to the length of the curved road surface, and the second width is less than or equal to the the width of the surface driving surface;
  • a length of the first length and a width of the to-be-run surface area of the second width are determined from the curved running surface.
  • one side of the road surface area to be driven is coincident with the side of the curved road surface area close to the mobile platform.
  • the mobile platform in this embodiment can be used to execute the technical solutions in the foregoing method embodiments, and the implementation principles and technical effects thereof are similar, and details are not described herein again.
  • Embodiments of the present application further provide a mobile platform, where a point cloud sensor is mounted on the mobile platform, and the point cloud sensor can execute the methods in the foregoing method embodiments.
  • FIG. 15 is a schematic block diagram of an electronic device according to an embodiment of the present application.
  • the electronic device in this embodiment may be a mobile platform, or a chip, a chip system, or a processor that supports the mobile platform to implement the above method; the electronic device may be a point cloud sensor, or a point cloud sensor that supports the implementation of the above method chip, system-on-chip, or processor.
  • the electronic device in this embodiment can be used to implement the method described in the foregoing method embodiment, and for details, reference may be made to the description in the foregoing method embodiment.
  • the electronic device may include one or more processors 1501, and the processors 1501 may also be referred to as processing units, which may implement certain control functions.
  • the processor 1501 may be a general-purpose processor or a special-purpose processor, or the like.
  • the processor 1501 may also store instructions and/or data 1503, and the instructions and/or data 1503 may be executed by the processor, so that the electronic device executes the above method embodiments method described.
  • the processor 1501 may include a transceiver unit for implementing receiving and transmitting functions.
  • the transceiver unit may be a transceiver circuit, or an interface, or an interface circuit.
  • Transceiver circuits, interfaces or interface circuits used to implement receiving and transmitting functions may be separate or integrated.
  • the above-mentioned transceiver circuit, interface or interface circuit can be used for reading and writing code/data, or the above-mentioned transceiver circuit, interface or interface circuit can be used for signal transmission or transmission.
  • the electronic device may include one or more memories 1502 on which instructions 1504 may be stored, and the instructions may be executed on the processor, so that the electronic device executes the above method embodiments method described.
  • data may also be stored in the memory.
  • instructions and/or data may also be stored in the processor.
  • the processor and the memory can be provided separately or integrated together. For example, the corresponding relationship described in the above method embodiments may be stored in a memory or in a processor.
  • the electronic device may further include a transceiver 1505 and/or an antenna 1506 .
  • the processor 1501 may be referred to as a processing unit, and controls the electronic device.
  • the transceiver 1505 may be referred to as a transceiver unit, a transceiver, a transceiver circuit or a transceiver, etc., and is used to implement a transceiver function.
  • An embodiment of the present application further provides a storage medium, characterized in that, the storage medium includes a computer program, and the computer program is used to implement the method in the foregoing method embodiment.
  • CMOS complementary metal oxide semiconductor
  • NMOS nMetal-oxide-semiconductor
  • PMOS P-type metal oxide semiconductor
  • BJT bipolar junction transistor
  • BiCMOS bipolar CMOS
  • SiGe silicon germanium
  • GaAs gallium arsenide
  • the processor in this embodiment of the present application may be an integrated circuit chip, which has a signal processing capability.
  • each step of the above method embodiments may be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.
  • the above-mentioned processor may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other possible Programming logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • the memory in this embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically programmable Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • Volatile memory may be random access memory (RAM), which acts as an external cache.
  • RAM random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous DRAM
  • SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • direct rambus RAM direct rambus RAM
  • the above-described embodiments are implemented using software, they may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated.
  • the computer may be a general purpose computer, special purpose computer, computer network, or other programmable device.
  • the computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server, or data center Transmission to another website site, computer, server, or data center by wire (eg, coaxial cable, optical fiber, digital subscriber line, DSL) or wireless (eg, infrared, wireless, microwave, etc.).
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media.
  • the available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, high-density digital video discs (DVDs)), or semiconductor media (eg, solid state disks, SSD)) etc.
  • the disclosed system, apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution.
  • the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the character "/" generally indicates that the associated objects are an "or” relationship.
  • At least one of or “at least one of” herein mean all or any combination of the listed items, eg, "at least one of A, B, and C", It can be expressed as: A alone exists, B alone exists, C alone exists, A and B exist simultaneously, B and C exist simultaneously, and A, B and C exist simultaneously, where A can be singular or plural, and B can be Singular or plural, C can be singular or plural.
  • B corresponding to A means that B is associated with A, and B can be determined according to A.
  • determining B according to A does not mean that B is only determined according to A, and B may also be determined according to A and/or other information.

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  • Theoretical Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
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Abstract

Procédé et dispositif de génération de carte de grille d'occupation. Le procédé consiste : à acquérir des nuages de points d'un environnement ambiant collecté par un capteur de nuage de points (S801) ; à acquérir un nuage de points d'obstacle et un nuage de points de masse des nuages de points de l'environnement ambiant (S802) ; à ajuster une chaussée de conduite incurvée selon des caractéristiques du nuage de points de sol et du sol, le sol étant le sol d'une route sur laquelle se trouve une plateforme mobile, et le capteur de nuage de points étant porté sur la plateforme mobile (S803) ; à déterminer, à partir de la chaussée de conduite incurvée, une zone de chaussée prête à la circulation, la zone de chaussée prête à la circulation étant divisée en une pluralité de grilles (S804) ; à déterminer, en fonction du nuage de points d'obstacle, la probabilité que chaque grille soit occupée (S805) ; et à générer une carte de grille d'occupation incurvée en fonction de la probabilité que chaque grille soit occupée et que la zone de chaussée soit prête à la circulation (S806).
PCT/CN2020/118950 2020-09-29 2020-09-29 Procédé et dispositif de génération de carte de grille d'occupation WO2022067534A1 (fr)

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