Systems and methods for radar localization in autonomous vehicles

Patent metadata

Field	Value
Publication number	US-10528057-B2
Application number	US-201715714750-A
Country	US
Kind code	B2
Filing date	Sep 25, 2017
Priority date	Sep 25, 2017
Publication date	Jan 7, 2020
Grant date	Jan 7, 2020

Abstract

Systems and method are provided for controlling a vehicle. In one embodiment, a localization method includes receiving sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes, and constructing a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes. The method further includes defining, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location, and determining, with a processor, a feature correlation between the first set of features and a second, previously determined set of features.

First claim

What is claimed is:

1. A localization method comprising:
receiving sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes;
constructing a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on a machine learning clustering method and their corresponding attributes;
defining, for each of the first plurality of sensor data groups, a first set of features, wherein the first set of features includes a histogram associated with each of the corresponding attributes, and wherein each group has an associated feature location;
determining, with a processor, a feature correlation between the first set of features and a second, previously determined set of features based on the histograms and the feature location; and
estimating a position of the vehicle based on the feature correlation.

2. The method of claim 1 , wherein the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity.

3. The method of claim 1 , wherein the sensor data includes at least radar data.

4. The method of claim 1 , wherein the first set of features includes a convex hull of the histogram.

5. The method of claim 1 , wherein the first set of features includes a summary statistic of one of the corresponding attributes.

6. The method of claim 1 , wherein determining the feature correlation includes performing an outlier removal procedure with respect to the first and second sets of features.

7. The method of claim 6 , wherein the outlier removal procedure is a random sample consensus (RANSAC) procedure.

8. The method of claim 1 , further including classifying each of the sensor data groups as being associated with one of a dynamic object, a static-moveable object, and a static-nonmoveable object, and determining the feature correlation based only on the sensor data groups associated with static-nonmoveable objects.

9. A system for controlling a vehicle, comprising:
a feature determination module, including a processor, configured to: receive sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; construct a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on a machine learning clustering method and their corresponding attributes; and define, for each of the first plurality of sensor data groups, a first set of features, the first set of features includes a histogram associated with each of the corresponding attributes, and wherein each group has an associated feature location; and
a feature correlation module configured to determine, with a processor, a feature correlation between the first set of features and a second, previously determined set of features based on the histograms and the feature location.

10. The system of claim 9 , wherein:
the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity; and
the sensor data is at least one of radar data and lidar data.

11. The system of claim 9 , wherein the first set of features includes a summary statistic of one of the corresponding attributes.

12. The system of claim 9 , wherein the feature correlation module performs an outlier removal procedure with respect to the first and second sets of features.

13. The system of claim 12 , wherein the outlier removal procedure is a random sample consensus (RANSAC) procedure.

14. The system of claim 9 , wherein the feature determination module classifies each of the sensor data groups as being associated with one of a dynamic object, a static-moveable object, and a static-nonmoveable object, and the feature correlation module determines the feature correlation based only on the sensor data groups associated with static-nonmoveable objects.

15. An autonomous vehicle, comprising:
at least one sensor that provides sensor data relating to an environment of the autonomous vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; and
a controller that, by a processor:
receives the sensor data;
constructs a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on a machine learning clustering method and their corresponding attributes;
defines, for each of the first plurality of sensor data groups, a first set of features, wherein the first set of features includes a histogram associated with each of the corresponding attributes, and wherein each group has an associated feature location;
determines, with a processor, a feature correlation between the first set of features and a second, previously determined set of features based on the histograms and the feature location; and
estimates a position of the vehicle based on the feature correlation.

16. The autonomous vehicle of claim 15 , wherein the controller performs outlier removal via a random sample consensus (RANSAC) procedure to determine the feature correlation.

17. The autonomous vehicle of claim 15 , wherein
the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity; and
the sensor data includes radar data.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Branson Elliot

Classifications

• G01S13/06

  Systems determining position data of a target · CPC title

• G01S13/867

  Combination of radar systems with cameras · CPC title

• G01S17/931

  of land vehicles · CPC title

• G01S2013/9318

  Controlling the steering · CPC title

• G01S13/865

  Combination of radar systems with lidar systems · CPC title