Lidar sensor denoising for adverse conditions and/or nonsalient objects

Patent metadata

Field	Value
Publication number	US-12411247-B2
Application number	US-202318240933-A
Country	US
Kind code	B2
Filing date	Aug 31, 2023
Priority date	Aug 31, 2023
Publication date	Sep 9, 2025
Grant date	Sep 9, 2025

Abstract

Particulate matter, such as fog, snow, rain, steam, vehicle exhaust, debris (plastic bags), etc. may cause one or more sensor types to generate false positive solid surface detections. In particular, various depth measurements may be impeded by particulate matter. Identifying false positive return(s) may comprise clustering lidar points, determining differences in range indicated by two different lidar devices having lidar points in the cluster, determining first differences that are more negative than a negative difference threshold and second differences that are more positive than a positive difference threshold, determining a first portion of lidar data in the cluster associated with the first differences and the second differences is associated with particulate matter or debris, and controlling a vehicle based at least in part on suppressing the first portion of the lidar data or indicating that the first portion of the lidar data is associated with particulate matter or debris.

First claim

What is claimed is:

1. A system comprising:
one or more processors; and
one or more one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
receiving lidar data;
determining, based at least in part on distances between points of the lidar data, a cluster of lidar points;
determining a first subset of the cluster that was received from a first lidar device;
determining a second subset of the cluster that was received from a second lidar device;
determining differences in range between points of the first subset and the second subset, wherein a first difference of the differences indicates a disparity between a first range indicated by a first lidar point of the first subset and a second range indicated by a second lidar point of the second subset;
determining that the differences comprise a first subset of differences that are more negative than a first negative threshold difference and a second subset of differences that are more positive than a first positive threshold difference;
determining, based at least in part on the first subset of differences and the second subset of differences, that a first portion of lidar data in the cluster is associated with particulate matter or debris, wherein the first portion of lidar data is associated with lidar points from which the first subset of differences and the second subset of differences were determined; and
controlling a vehicle based at least in part on suppressing the first portion of lidar data.

2. The system of claim 1 , wherein:
controlling the vehicle is based at least in part on a second portion of lidar data in the cluster,
determining the second portion of lidar data includes determining the second portion of lidar data is associated with an object relevant to vehicle travel,
determining the second portion of lidar comprises determining the second portion of lidar data is associated with at least one of second negative differences that are less than a second negative threshold difference or second positive differences that are less than a second positive threshold difference,
the second negative threshold difference may be the same or less than the first negative threshold difference, and
the second positive threshold difference may be the same or less than the first positive threshold difference.

3. The system of claim 1 , wherein determining that the first portion of lidar data is associated with particulate matter or debris is further based at least in part on:
determining a distribution of the differences in range;
determining a standard deviation of the distribution;
determining a ratio of the standard deviation to a nominal standard deviation associated with solid objects; and
determining that the ratio meets or exceeds a threshold ratio.

4. The system of claim 1 , wherein determining that the first portion of lidar data is associated with particulate matter or debris is further based at least in part on:
determining a distribution of the differences in range; and
determining the distribution is tri-modal.

5. The system of claim 1 , wherein determining that the first portion of lidar data is associated with particulate matter or debris is further based at least in part on:
determining a distribution of the differences in range;
determining a standard deviation or variance of at least one of the first subset of differences and the second subset of differences; and

determining the portion of the cluster that is associated with the particulate matter or debris comprises determining that the standard deviation or the variance meets or exceeds a threshold standard deviation or a threshold variance.

6. One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving lidar data;
determining, based at least in part on the lidar data, a cluster of lidar points including a first subset of lidar data received from a first lidar device and a second subset of lidar data received from a second lidar device;
determining differences in range between points of the first subset and the second subset, wherein a first difference of the differences indicates a disparity between a first range indicated by a first lidar point of the first subset and a second range indicated by a second lidar point of the second subset;
determining that the differences comprise a first subset of differences that meet or exceed a first negative threshold difference and a second subset of differences that meet or exceed a first positive threshold difference;
determining, based at least in part on the first subset of differences and the second subset of differences, that a first portion of lidar data in the cluster is associated with particulate matter or debris, wherein the first portion of lidar data is associated with lidar points from which the first subset of differences and second subset of differences were determined; and
controlling a vehicle based at least in part on suppressing the first portion of lidar data or indicating that the first portion of lidar data is associated with particulate matter or debris.

7. The one or more non-transitory computer-readable media of claim 6 , wherein:
controlling the vehicle is based at least in part on a second portion of lidar data in the cluster,
determining the second portion of lidar data includes determining the second portion of lidar data is associated with an object relevant to vehicle travel,
determining the second portion of lidar comprises determining the second portion of lidar data is associated with at least one of second negative differences that are less than a second negative threshold difference or second positive differences that are less than a second positive threshold difference,
the second negative threshold difference may be the same or less than the first negative threshold difference, and
the second positive threshold difference may be the same or less than the first positive threshold difference.

8. The one or more non-transitory computer-readable media of claim 6 , wherein determining that the first portion of lidar data is associated with particulate matter or debris is further based at least in part on:
determining a distribution of the differences in range;
determining a standard deviation of the distribution;
determining a ratio of the standard deviation to a nominal standard deviation associated with solid objects; and
determining that the ratio meets or exceeds a threshold ratio.

9. The one or more non-transitory computer-readable media of claim 6 , wherein determining that the first portion of lidar data is associated with particulate matter or debris is further based at least in part on:
determining a distribution of the differences in range; and
determining the distribution is tri-modal.

10. The one or more non-transitory computer-readable media of claim 6 , wherein determining that the first portion of lidar data is associated with particulate matter or debris is further based at least in part on:
determining a distribution of the differences in range;
determining a standard deviation or variance of at least one of the first subset of differences and the second subset of differences; and

determining the portion of the cluster that is associated with the particulate matter or debris comprises determining that the standard deviation or the variance meets or exceeds a threshold standard deviation or a threshold variance.

11. The one or more non-tr

Assignees

• Zoox Inc

Inventors

• Terefe Nehemia Girma

Classifications

• B60W2420/408

  Radar; Laser, e.g. lidar · CPC title

• B60W50/029

  Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts · CPC title

• B60W50/0205

  Diagnosing or detecting failures; Failure detection models · CPC title

• B60W2050/0215

  Sensor drifts or sensor failures · CPC title

• G01S7/4802

  using analysis of echo signal for target characterisation; Target signature; Target cross-section · CPC title