Ground Plane Estimation in a Computer Vision System

Patent metadata

Field	Value
Publication number	US-2017191826-A1
Application number	US-201615255832-A
Country	US
Kind code	A1
Filing date	Sep 2, 2016
Priority date	Jan 5, 2016
Publication date	Jul 6, 2017
Grant date	—

Abstract

Estimation of the ground plane of a three dimensional (3D) point cloud based modifications to the random sample consensus (RANSAC) algorithm is provided. The modifications may include applying roll and pitch constraints to the selection of random planes in the 3D point cloud, using a cost function based on the number of inliers in the random plane and the number of 3D points below the random plane in the 3D point cloud, and computing a distance threshold for the 3D point cloud that is used in determining whether or not a 3D point in the 3D point cloud is an inlier of a random plane.

First claim

What is claimed is:

1 . A method for ground plane estimation in a three dimensional (3D) point cloud in a computer vision system, the method comprising:
receiving a 3D point cloud generated based on a plurality of 2D frames captured by a monocular camera; determining a distance threshold for the 3D point cloud based on an estimated height of a ground plane in the 3D point cloud; and estimating the ground plane of the 3D point cloud by performing the following for a predetermined number of iterations:
identifying a random plane in the 3D point cloud from three randomly selected non-collinear 3D points in the 3D point cloud, wherein an incline of the random plane meets predetermined pitch and roll constraints;
computing a cost function of the random plane, wherein the cost function is based on a number of inliers of the random plane and a number of 3D points below the random plane, wherein the distance threshold is used to determine whether or not a 3D point in the 3D point cloud is an inlier; and
saving the cost function as a best cost function if the cost function is better than a previously computed cost function for a previously identified random plane.

2 . The method of claim 1 , further comprising pruning the 3D point cloud prior to estimating the ground plane to eliminate 3D points not likely to be on the ground plane.

3 . The method of claim 2 , wherein pruning comprises sorting the 3D points in the 3D point cloud according to values of Y coordinates, and eliminating all 3D points from the 3D point cloud not included in a predetermined percentage of the sorted 3D points having the highest Y coordinate values.

4 . The method of claim 3 , wherein the predetermined percentage is in a range of 45% to 55%.

5 . The method of claim 1 , wherein computing a cost function comprises computing the cost function as the number of inliers minus the number of 3D points below the random plane.

6 . The method of claim 1 , wherein computing a cost function comprises giving more weight to inliers of the random plane and 3D points below the plane that also lie in a predetermined trapezoid of a 2D frame of the plurality of 2D frames.

7 . The method of claim 6 , wherein computing a cost function comprises computing the cost function as (a number of the inliers outside the trapezoid−a number of 3D points below the random plane outside the trapezoid)+w*(a number of the inliers inside the trapezoid−a number of 3D points below the plane inside the trapezoid), wherein w is a predetermined weight factor.

8 . The method of claim 1 , wherein determining a distance threshold comprises determining a reference height for the ground plane based on 3D points in the 3D point cloud likely to be in the ground plane, and computing the distance threshold as a predetermined fraction of the reference height.

9 . The method of claim 8 , wherein the predetermined fraction is based on the height of the monocular camera and a target distance threshold.

10 . The method of claim 8 , wherein determining a reference height comprises sorting the 3D points in the 3D point cloud according to values of Y coordinates, and selecting a value of a Y coordinate of a 3D point at a predetermined percentile of the sorted 3D points as the reference height, wherein the predetermined percentile compensates for noise in the generation of the 3D point cloud.

11 . A computer vision system comprising:
a monocular camera configured to capture a plurality of two dimensional (2D) frames of a scene; and a processor configured to receive a three dimensional (3D) point cloud generated based on the plurality of 2D frames, the processor configured to:
determine a distance threshold for the 3D point cloud based on an estimated height of a ground plane in the 3D point cloud; and
estimate the ground plane of the 3D point cloud by performing the following for a predetermined number of iterations:
identifying a random plane in the 3D point cloud from three randomly selected non-collinear 3D points in the 3D point cloud, wherein an incline of the random plane meets predetermined pitch and roll constraints;
computing a cost function of the random plane, wherein the cost function is based on a number of inliers of the random plane and a number of 3D points below the random plane, wherein the distance threshold is used to determine whether or not a 3D point in the 3D point cloud is an inlier; and
saving the cost function as a best cost function if the cost function is better than a previously computed cost function for a previously identified random plane.

12 . The computer vision system of claim 11 , wherein the processor is further configured to prune the 3D point cloud prior to estimating the ground plane to eliminate 3D points not likely to be on the ground plane.

13 . The computer vision system of claim 12 , wherein the processor is configured to prune the 3D point cloud by sorting the 3D points in the 3D point cloud according to values of Y coordinates, and eliminating all 3D points from the 3D point cloud not included in a predetermined percentage of the sorted 3D points having the highest Y coordinate values.

14 . The computer vision system of claim 13 , wherein the predetermined percentage is in a range of 45% to 55%.

15 . The computer vision system of claim 11 , wherein computing the cost function comprises computing the cost function as the number of inliers minus the number of 3D points below the random plane.

16 . The computer vision system of claim 11 , wherein computing the cost function comprises giving more weight to inliers of the random plane and 3D points below the plane that also lie in a predetermined trapezoid of a 2D frame of the plurality of 2D frames.

17 . The computer vision system of claim 16 , wherein computing the cost function comprises computing the cost function as (a number of the inliers outside the trapezoid−a number of 3D points below the random plane outside the trapezoid)+w*(a number of the inliers inside the trapezoid−a number of 3D points below the plane inside the trapezoid), wherein w is a predetermined weight factor.

18 . The computer vision system of claim 11 , wherein the processor is further configured to determine a distance threshold by determining a reference height for the ground plane based on 3D points in the 3D point cloud likely to be in the ground plane, and computing the distance threshold as a predetermined fraction of the reference height.

19 . The computer vision system of claim 18 , wherein the predetermined fraction is based on the height of the monocular camera and a target distance threshold.

20 . The computer vision system of claim 18 , wherein determining a reference height comprises sorting the 3D points in the 3D point cloud according to values of Y coordinates, and selecting a value of a Y coordinate of a 3D point at a predetermined percentile of the sorted 3D points as the reference height, wherein the predetermined percentile compensates for noise in the generation of the 3D point cloud.

Assignees

• Texas Instruments Inc

Inventors

• Nagori Soyeb
• Kumar Poorna
• Mathew Manu
• Viswanath Prashanth Ramanathpur
• Poddar Deepak Kumar

Classifications

• G06V20/56 Primary

  exterior to a vehicle by using sensors mounted on the vehicle · CPC title

• G01C3/08 Primary

  Use of electric radiation detectors · CPC title

• G06F18/2113

  by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation · CPC title

• B60R11/04

  Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle · CPC title

• G06T7/0042

  Physics · mapped topic