Localization with negative mapping

Patent metadata

Field	Value
Publication number	US-10761541-B2
Application number	US-201715494159-A
Country	US
Kind code	B2
Filing date	Apr 21, 2017
Priority date	Apr 21, 2017
Publication date	Sep 1, 2020
Grant date	Sep 1, 2020

Abstract

Example embodiments include determining a map of an environment of a robotic vehicle. The map includes locations of a plurality of mapped landmarks within the environment and a false detection source region within the environment. The embodiments further include detecting a plurality of candidate landmarks, and determining which of the detected candidate landmarks correspond to one of the plurality of mapped landmarks and which correspond to false detections. The embodiments additionally include estimating a pose of the robotic vehicle within the environment. The embodiments further include determining which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region. The embodiments still further include determining a confidence level of the pose estimate based on which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region.

First claim

What is claimed is:

1. A method comprising:
determining a map of an environment, wherein the map comprises locations of a plurality of mapped landmarks within the environment and a false detection source region within the environment, wherein the false detection source region indicates a portion of the environment from which false detections are expected;
detecting a plurality of candidate landmarks based on sensor data from a sensor;
determining which of the plurality of candidate landmarks correspond to one of the plurality of mapped landmarks and which correspond to false detections;
estimating a pose of a robotic vehicle within the environment based on the plurality of candidate landmarks determined to correspond to one of the plurality of mapped landmarks;
determining, based on the estimated pose of the robotic vehicle, which of the plurality of candidate landmarks determined to correspond to false detections fall within the false detection source region; and
determining a confidence level of the pose estimate of the robotic vehicle based on which of the plurality of candidate landmarks determined to correspond to false detections fall within the false detection source region, wherein determining that a given candidate landmark that corresponds to a false detection does not fall within the false detection source region lowers the confidence level.

2. The method of claim 1 , further comprising navigating the robotic vehicle through the environment based on the estimated pose of the robotic vehicle and the confidence level of the pose estimate.

3. The method of claim 1 , wherein determining the mapped false detection source region comprises:
determining a plurality of false detections;
determining a false detection source location associated with each false detection; and
determining the mapped false detection source region based on the determined false detection source locations.

4. The method of claim 3 , wherein determining the mapped false detection source region based on the determined false detection source locations comprises determining a region comprising a number of false detections per area unit that meets or exceeds a false detection source region threshold.

5. The method of claim 3 , wherein determining the mapped false detection source region based on the false detection source locations comprises defining an area surrounding each false detection source location as being part of the false detection source region.

6. The method of claim 1 , wherein detecting the plurality of candidate landmarks comprises receiving, by the sensor, signals from a plurality of signal sources within the environment, and determining which of the received signals comprises a signal strength that meets or exceeds a candidate landmark signal strength threshold.

7. The method of claim 1 , wherein determining which of the candidate landmarks correspond to mapped landmarks comprises applying a transformation to the plurality of candidate landmarks that aligns the candidate landmarks with the mapped landmarks and determining which of the transformed candidate landmarks fall within an inlier distance threshold of one of the mapped landmarks, wherein the inlier distance threshold corresponds to a radius surrounding a mapped landmark.

8. The method of claim 7 , wherein determining which of the candidate landmarks correspond to false detections comprises determining a remainder of candidate landmarks that do not correspond to one of the plurality of mapped landmarks.

9. The method of claim 1 , wherein the sensor data from the sensor comprises source locations of the candidate landmarks relative to the robotic vehicle, and wherein estimating the pose of the robotic vehicle comprises:
applying a transformation to each of the candidate landmarks determined to correspond to mapped landmarks that aligns each such candidate landmark with a corresponding mapped landmark; and
determining a pose of the robotic vehicle relative to the aligned candidate landmarks based on the source locations of the candidate landmarks relative to the robotic vehicle.

10. The method of claim 9 , wherein determining which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region comprises applying the transformation to each such candidate landmark and determining which of the transformed candidate landmarks fall within the false detection source region.

11. The method of claim 1 , wherein determining the confidence level of the pose estimate based on which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region comprises:
determining a total number of candidate landmarks; and
determining the confidence level of the pose estimate based on a ratio of candidate landmarks that (i) correspond to a mapped landmark or that (ii) fall within a false detection source region to the total number of candidate landmarks.

12. A system, comprising:
a robotic vehicle
a sensor mounted on the robotic vehicle;
one or more processors;
a non-transitory computer-readable medium; and
program instructions stored on the non-transitory computer readable medium and executable by the one or more processors to:
determine a map of an environment, wherein the map comprises locations of a plurality of mapped landmarks within the environment and a false detection source region within the environment, wherein the false detection source region indicates a portion of the environment from which false detections are expected;
detect a plurality of candidate landmarks based on sensor data from the sensor;
determine which of the detected candidate landmarks correspond to one of the plurality of mapped landmarks and which correspond to false detections;
estimate a pose of the robotic vehicle within the environment based on the plurality of candidate landmarks determined to correspond to one of the plurality of mapped landmarks;
determine, based on the estimated pose of the robotic vehicle, which of the plurality of candidate landmarks determined to correspond to false detections fall within the false detection source region; and
determine a confidence level of the pose estimate of the robotic vehicle based on which of the plurality of candidate landmarks determined to correspond to false detections fall within the false detection source region, wherein determining that a given candidate landmark that corresponds to a false detection does not fall within the false detection source region lowers the confidence level.

13. The system of claim 12 , wherein the sensor comprises a light detection and ranging (LIDAR) unit configured to send a signal to a portion of the environment, and wherein the sensor is configured to detect reflected signals from sources within the portion of the environment.

14. The system of claim 12 , wherein the landmarks placed within the environment comprise retroreflective markers.

15. The system of claim 12 , wherein the one or more processors are comprised within the robotic vehicle.

16. The system of claim 12 , further comprising a remote controller of the robotic vehicle, wherein a processor of the one or more processors is comprised within the robotic vehicle and a processor of the one or more processors is comprised within the remote controller of the robotic vehicle.

17. A non-transitory computer readable medium having stored thereon instructions executable by one or more processors to cause a computing system to perform functions comprising:
determining a map of an environmen

Assignees

• X Dev Llc

Inventors

• Holz Dirk

Classifications

• G01S17/89

  for mapping or imaging · CPC title

• G01S17/86

  Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders · CPC title

• Y10S901/01

  Mobile robot · CPC title

• G01S17/42

  Simultaneous measurement of distance and other co-ordinates (indirect measurement G01S17/46) · CPC title

• B66F9/0755

  Position control; Position detectors · CPC title