US 12,430,797 B2
Localization method and apparatus, electronic device, and storage medium
Minzhao Zhu, Beijing (CN); Tao Kong, Beijing (CN); and Lei Li, Beijing (CN)
Assigned to BEIJING YOUZHUJU NETWORK TECHNOLOGY CO., LTD., Beijing (CN)
Appl. No. 18/566,500
Filed by BEIJING YOUZHUJU NETWORK TECHNOLOGY CO., LTD., Beijing (CN)
PCT Filed May 27, 2022, PCT No. PCT/CN2022/095453
§ 371(c)(1), (2) Date Dec. 1, 2023,
PCT Pub. No. WO2022/253113, PCT Pub. Date Dec. 8, 2022.
Claims priority of application No. 202110609933.0 (CN), filed on Jun. 1, 2021.
Prior Publication US 2024/0249430 A1, Jul. 25, 2024
Int. Cl. G06T 7/73 (2017.01); G01C 21/00 (2006.01)
CPC G06T 7/73 (2017.01) [G01C 21/005 (2013.01); G06T 2207/10028 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A localization method, comprising:
acquiring a target local semantic point cloud map at a current position, global semantic grid maps at two or more levels, and a laser point cloud map, wherein the global semantic grid maps at different levels have different resolutions, wherein each grid of the global semantic grid map records a value of probability that a target semantic object exists in the grid;
performing pose identification on the basis of the target local semantic point cloud map and the global semantic grid map to obtain a set of candidate poses; and
determining a target pose according to the laser point cloud map and the set of candidate poses, wherein the target pose comprises a loop closure pose and/or a relocalization pose,
wherein, the performing pose identification on the basis of the target local semantic point cloud map and the global semantic grid map to obtain a set of candidate poses, comprises:
acquiring a target grid stack comprising grids with a probability value of greater than zero in the global semantic grid map at the highest level, wherein the level of each global semantic grid map is negatively correlated with the resolution;
calculating, based on the target local semantic point cloud map, a first evaluation index for each grid in the target grid stack and a first evaluation index value for at least one grid in another level of global semantic grid map, and determining, based on the first evaluation index value, a candidate pose to obtain a set of candidate poses,
wherein, the calculating, based on the target local semantic point cloud map, a first evaluation index for each grid in the target grid stack and a first evaluation index value for at least one grid in another level of global semantic grid map, and determining, based on the first evaluation index value, a candidate pose to obtain a set of candidate poses, comprises:
constructing a current map identical to the target local semantic point cloud map;
creating a current grid stack identical to the target grid stack, and controlling a current grid located at the top of the current grid stack to pop;
calculating a first evaluation index value for the current grid based on the current map; and
determining the candidate pose based on the grid for which the first evaluation index value is greater than a first index threshold.