Method and apparatus for classifying LIDAR data for object detection

Patent metadata

Field	Value
Publication number	US-10346695-B2
Application number	US-201715605275-A
Country	US
Kind code	B2
Filing date	May 25, 2017
Priority date	May 25, 2017
Publication date	Jul 9, 2019
Grant date	Jul 9, 2019

Abstract

A method and apparatus for classifying light detection and ranging sensor data are provided. The method includes transforming sensor data of the LIDAR into point cloud data, selecting a cell including a subset of the point cloud data, dividing the selected cell into a plurality of voxels, calculating a difference of gradients for the plurality of voxels, performing a first pass on the plurality of voxels to identify voxels that contain an object based the difference of gradients, performing a second pass on the plurality of voxels to identify voxels that contain the object by adjusting a voxel with at least one from among a jitter parameter and a rotation parameter, and outputting a centroid average of voxels identified as containing the object.

First claim

What is claimed is:

1. A method for classifying light detection and ranging (LIDAR) sensor data, the method comprising:
transforming sensor data of the LIDAR into point cloud data;
selecting a cell including a subset of the point cloud data;
dividing the selected cell into a plurality of voxels;
calculating a difference of gradients for the plurality of voxels;
performing a first pass on the plurality of voxels to identify voxels that contain an object based on the difference of gradients;
performing a second pass on the plurality of voxels to identify voxels that contain the object by adjusting a voxel with at least one from among a jitter parameter and a rotation parameter; and
outputting a centroid average of voxels identified as containing the object,
wherein the calculating the difference of gradients of the plurality of voxels comprises calculating a difference between a center voxel of the cell and all other voxels in the cell.

2. The method of claim 1 , wherein the point cloud data comprises at least one from among LASer (LAS) file format and point cloud data (PCD) file format.

3. The method of claim 1 , wherein the point cloud data comprises x coordinate data, y coordinate data, z coordinate data and a reflective intensity value.

4. The method of claim 1 , wherein the performing the second pass on the plurality of voxels to identify voxels that contain the object comprises:
adjusting a voxel with the rotation parameter by rotating points of the voxels;
comparing the adjusted voxel to clustered data;
outputting a centroid average of the adjusted voxel identified as containing the object if the comparing indicates the adjusted voxel contains the object.

5. The method of claim 1 , wherein the performing the second pass on the plurality of voxels to identify voxels that contain the object comprises:
adjusting a voxel with the jitter parameter by translating points of the voxel;
comparing the adjusted voxel to clustered data;
outputting a centroid average of the adjusted voxel identified as containing the object if the comparing indicates the adjusted voxel contains the object.

6. The method of claim 1 , wherein the object comprises at least one from among a traffic control device, a traffic control sign, and a traffic control light.

7. The method of claim 1 , further comprising receiving the sensor data from a LIDAR sensor.

8. The method of claim 1 , further comprising dividing the point cloud data into a plurality of cells,
wherein the selecting the cell comprises selecting a cell from among the plurality of cells.

9. A non-transitory computer readable medium comprising computer instructions executable to perform the method of claim 1 .

10. An apparatus that classifies light detection and ranging (LIDAR) sensor data, the apparatus comprising:
at least one memory comprising computer executable instructions; and
at least one processor configured to read and execute the computer executable instructions, the computer executable instructions causing the at least one processor to:
transform sensor data of the LIDAR into point cloud data;
select a cell including a subset of the point cloud data;
divide the selected cell into a plurality of voxels;
calculate a difference of gradients for the plurality of voxels;
perform a first pass on the plurality of voxels to identify voxels that contain an object based on the difference of gradients;
perform a second pass on the plurality of voxels to identify voxels that contain the object by adjusting a voxel with at least one from among a jitter parameter and a rotation parameter; and
output a centroid average of voxels identified as containing the object,
wherein the computer executable instructions further cause the at least one processor to calculate the difference of gradients of the plurality of voxels by calculating a difference between a center voxel of the cell and all other voxels in the cell.

11. The apparatus of claim 10 , wherein the point cloud data comprises at least one from among LASer (LAS) file format and point cloud data (PCD) file format.

12. The apparatus of claim 10 , wherein the point cloud data comprises x coordinate data, y coordinate data, z coordinate data and a reflective intensity value.

13. The apparatus of claim 10 , wherein the computer executable instructions cause the at least one processor to perform the second pass on the plurality of voxels to identify voxels that contain the object by:
adjusting a voxel with the rotation parameter by rotating points of the voxels;
comparing the adjusted voxel to clustered data; and
outputting a centroid average of the adjusted voxel identified as containing the object if the comparing indicates the adjusted voxel contains the object.

14. The apparatus of claim 10 , wherein the computer executable instructions further cause the at least one processor to perform the second pass on the plurality of voxels to identify voxels that contain the object by:
adjusting a voxel with the jitter parameter by translating points of the voxel;
comparing the adjusted voxel to clustered data; and
outputting a centroid average of the adjusted voxel identified as containing the object if the comparing indicates the adjusted voxel contains the object.

15. The apparatus of claim 10 , wherein the object comprises at least one from among a traffic control device, a traffic control sign, and a traffic control light.

16. The apparatus of claim 10 , further comprising a LIDAR sensor,
wherein the computer executable instructions further cause the at least one processor to receive the sensor data from a LIDAR sensor.

17. The apparatus of claim 10 , wherein the computer executable instructions further cause the at least one processor to divide the point cloud data into a plurality of cells and select the cell by selecting a cell from among the plurality of cells.

18. The apparatus of claim 10 , wherein the computer executable instructions further cause the at least one processor to divide the point cloud data into a plurality of cells based on features of the point cloud data.

Assignees

• Gen Motors Llc

Inventors

• Clifford David H
• Hiemenga David J

Classifications

• G01S7/4808 Primary

  Evaluating distance, position or velocity data · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V20/582

  of traffic signs · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06F18/241 Primary

  relating to the classification model, e.g. parametric or non-parametric approaches · CPC title