Search

JP-7856805-B2 - Systems and methods for free-space estimation

JP7856805B2JP 7856805 B2JP7856805 B2JP 7856805B2JP-7856805-B2

Inventors

  • アビシェク ラヴィ
  • グレゴリー ジェイ． ビュイトクス
  • サイ ラヴィ テジャ ボガバラプ
  • ラアジタ グムマディ
  • デレク ケイン
  • ユー－シュアン チャン

Assignees

  • デカ・プロダクツ・リミテッド・パートナーシップ

Dates

Publication Date
20260511
Application Date
20250219
Priority Date
20190726

Claims (6)

  1. A method for assigning free-space probabilities in point cloud data for autonomous vehicle traverse, wherein the method is: The point cloud data is segmented into segments, Within the point cloud data, the location of a plane, a plane point within the plane, and a non-planar point associated with one of the plane points, Selecting one of the aforementioned planes as the surface plane for autonomous vehicle crossing, Based on the associated non-planar points, each of the planar points is classified as an obstacle point, Based on the associated non-planar points, the obstacle height for each obstacle point is determined, The blind distance to the surface plane is determined based on the intersection of projections from the autonomous vehicle onto the surface plane, For each cell along the line between the blind distance and the outer perimeter of the grid created from the surface plane , The initial probability of an obstacle occupying a cell along the aforementioned line is calculated based on the obstacle point within the cell, the position of the cell along the aforementioned line, and combinations thereof. The noise coefficient is calculated based on a first distance to the nearest obstacle along the line within the cell, a second distance to the cell along the line, the measurement significance of the cell along the line, the initial probability and default probability of the cell along the line, and combinations thereof, and A method comprising: calculating the current probability of an obstacle occupying a cell along the line based on the initial probability and the noise coefficient.
  2. Calculating the aforementioned initial probability is, If the sensor is unavailable and the cell is a neighboring cell located near the blind distance, then the initial probability of the cell is 1.0. If the sensor is unavailable and the cell is located between the neighboring cell and the outer perimeter, the initial probability of the cell is 0.5. If the sensor is available and at least one of the obstacle points is present in the cell, or if one of the cells along the previously encountered line contains at least one of the obstacle points, then the initial probability of the cell is 0.5. The method according to claim 1, wherein the initial probability of the cell is 0.3 if the sensor is available, there are no obstacle points in the cell, and none of the cells encountered along the line contain the obstacle points.
  3. Calculating the aforementioned noise coefficient is Includes, During the ceremony, d = the second distance, Z t = the first distance, The method according to claim 1, wherein σ = Z t 2 × 0.001.
  4. Calculating the current probability mentioned above is, The method according to claim 1, comprising the noise coefficient of the cell plus the initial probability of the cell.
  5. The method according to claim 1, wherein each of the aforementioned planes comprises the aforementioned non-planar points at a certain distance from the plane.
  6. A system for assigning free-space probabilities in point cloud data for autonomous vehicle traverse, wherein the system is A segment processor configured to segment the point cloud data, A planar processor configured to locate a plane, planar points within the point cloud data, and non-planar points associated with one of the planar points, An obstacle processor, wherein the obstacle processor is Selecting one of the aforementioned planes as the surface plane for autonomous vehicle crossing, Based on the associated non-planar points, each of the planar points is classified as an obstacle point, An obstacle processor is configured to determine the obstacle height associated with the obstacle point based on the non-planar point, A line sweep processor, wherein the line sweep processor is An initial probability processor, wherein the initial probability processor is Determining the blind distance, An initial probability processor is configured to perform the following for each cell along a line between the blind distance and the outer perimeter of a grid created from the surface plane: calculating the initial probability of an obstacle occupying the cell along the line, based on the obstacle point within the cell, the position of the cell along the line, and combinations thereof. A noise coefficient processor, wherein for each cell along the line between the blind distance and the outer circumference, A noise coefficient processor configured to calculate a noise coefficient based on a first distance to the nearest obstacle along the line within the cell, a second distance to the cell along the line, the measurement significance of the cell along the line, the initial probability of the cell along the line, the default probability, and a combination thereof. A current probability processor, the current probability processor configured to calculate the current probability of an obstacle point for each cell along each line between the blind distance and the outer perimeter, based on the initial probability and the noise coefficient, and a line sweep processor, the system comprising: The aforementioned system The system further comprises a sensor having a sensor beam configured to project from the sensor onto the surface plane, wherein the blind distance is determined based on the intersection of the sensor beam and the surface plane.

Description

(Cross-reference of related applications) This patent application claims the interests of U.S. Provisional Patent Application No. 62/879,391 (Patent Attorney No. AA027), filed on 26 July 2019, titled "System and Method for Free Space Estimation," which is incorporated herein by reference in its entirety. Vehicles travel on surfaces determined by their human operators to include free, unobstructed space. Humans use a complex set of criteria to determine whether to traverse a path within or over the vehicle. Considerations may include, but are not limited to, any number of factors such as ambient lighting, weather, and windshield issues, the degree of obstacle height, the amount of obstacle visible to the human, the area around the vehicle that is not visible to the human, and the degree to which the human visual system is accurate in detecting obstacles. Navigating obstacles in autonomous vehicles may require electronically evaluating some of the same complex criteria encountered routinely by human vehicle operators. Unobstructed (free) space must be rapidly determined from available sensor data about the autonomous vehicle in order to proceed continuously along the navigation path. Previously, free space was estimated from stereo camera data, from sequences of images in video acquired by camera systems, and from millimeter-wave radar, among other methods. What is needed is an efficient system that calculates the probability of the path being obstructed from sensor data. The sensors that collect the data can, for example, be mounted on an autonomous vehicle. What is needed is a system that takes into account the realities of road conditions and sensor limits. This instruction will be more easily understood by referring to the following explanation, which is accompanied by the diagrams. Figure 1A-1C is a flowchart of the process described in this instruction.Figure 1A-1C is a flowchart of the process described in this instruction.Figure 1A-1C is a flowchart of the process described in this instruction. Figure 2 is a schematic block diagram of the system described in this instruction. Figure 3 is a diagram illustrating the sensor configuration of this instruction. Figure 4 is a pictorial representation of the segmented point cloud data used in this instruction. Figure 5 is a picture of the plane within the segmented data of this instruction. Figure 6 is a pictorial representation of the normal vectors in this instruction. Figure 7 is a diagram illustrating the obstacle points in this instruction. Figure 8 is a diagram illustrating the obstacle grid, blind radius, and sensor beam for this instruction. Figure 9 is a diagram illustrating the initial probabilities of this instruction. Figure 9A is a diagram illustrating the probabilities when the sensor is unavailable. Figure 9B is a pictorial representation of the probabilities when the sensor is available. Figure 9C is a pictorial representation of the probabilities when a sensor is available and the cell contains an obstacle. Figure 10 is a diagram illustrating the current probabilities in this instruction. Figures 11A and 11B are flowcharts of the method described in this instruction.Figures 11A and 11B are flowcharts of the method described in this instruction. Figure 12A is a photographic representation of the LiDAR ring surrounding the autonomous vehicle. Figure 12B is a pictorial representation of the steps of the method described in this instruction. Figure 13 is a photographic representation of the point selection options on the LIDAR ring. Figure 14A is a photographic representation of point selections on adjacent LIDAR rings. Figure 14B is a pictorial representation of a further step in the method described in this instruction. Figure 15 is a photographic representation of phase 1 of the planar enlargement in this instruction. Figure 16 is a photographic representation of phase 2 of the planar enlargement in this instruction. Figure 17 is a pictorial representation of the LIDAR ring arc pattern formed by the ground plane discovered by the method described in this instruction. Figure 18 is a pictorial representation of the grid map update in this instruction. Figure 19 is a schematic block diagram of the second configuration of the system described in this instruction. Detailed explanation: The system and method of this instruction can estimate the free space surrounding an autonomous vehicle in real time. Referring here to Figure 1A-1C, the free space can be estimated from sensor data by sweeping 360° from a sensor placed at the center of the grid to the outer edge of the grid. Probabilities are calculated, and each cell is associated with the log odds of its occupancy probability. The log odds can be forced to zero for all cells that are within the blind radius and do not have a probability of being occupied. The blind radius is the area around the sensor that is blocked by the autonomous vehicle. If a cell has already been moved to during the sweep,