US 11,726,208 B2
Autonomous vehicle localization using a Lidar intensity map
Shenlong Wang, Toronto (CA); Andrei Pokrovsky, San Francisco, CA (US); Raquel Urtasun Sotil, Toronto (CA); and Ioan Andrei Bârsan, Toronto (CA)
Assigned to UATC, LLC, San Francisco, CA (US)
Filed by UATC, LLC, San Francisco, CA (US)
Filed on Jun. 11, 2019, as Appl. No. 16/437,827.
Claims priority of provisional application 62/685,875, filed on Jun. 15, 2018.
Prior Publication US 2019/0383945 A1, Dec. 19, 2019
Int. Cl. G01S 17/42 (2006.01); G01S 7/4861 (2020.01); G05D 1/00 (2006.01); G01S 19/51 (2010.01); G05D 1/02 (2020.01); G01S 17/931 (2020.01)
CPC G01S 17/42 (2013.01) [G01S 7/4861 (2013.01); G01S 17/931 (2020.01); G01S 19/51 (2013.01); G05D 1/0088 (2013.01); G05D 1/024 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An autonomous vehicle (AV) system configured to control a vehicle, the system comprising:
one or more processors of a machine; and
a machine-storage medium storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
generating, using a first embedding function, an intensity map embedding based on a Lidar intensity map, the Lidar intensity map comprising a map image encoded with Lidar intensity data, the intensity map embedding comprising a representation of the Lidar intensity map computed by the first embedding function, the map image comprising a birds-eye view (BEV) image of an environment;
generating, using a second embedding function, an online Lidar intensity embedding based on an online Lidar intensity image, the online lidar intensity image comprising a BEV rasterized image generated by aggregating point data output by a Lidar system during operation of the vehicle, the aggregating using IMU data and wheel odometer read information, the online Lidar intensity embedding comprising a representation of the online Lidar intensity image computed by the second embedding function, the point data comprising multiple point clouds;
transforming the online Lidar intensity embedding into a coordinate frame of the intensity map embedding;
generating a plurality of pose candidates based on the online Lidar intensity embedding;
computing a three-dimensional (3D) score map based on a comparison of the intensity map embedding with each pose candidate in the plurality of pose candidates, the 3D score map comprising a plurality of match scores, the plurality of match scores comprising a match score for a pose candidate in the plurality of pose candidates, the match score for the pose candidate indicating a similarity between the pose candidate and the map embedding;
determining a pose of the vehicle based on the 3D score map, the pose of the vehicle corresponding to the pose candidate, the pose of the vehicle comprising a longitude, a latitude, and a heading; and
controlling one or more operations of the vehicle based on the pose.