US 12,146,747 B2
Self-localization device
Masahiro Tomono, Chiba (JP)
Assigned to CHIBA INSTITUTE OF TECHNOLOGY, Chiba (JP)
Appl. No. 17/905,015
Filed by Chiba institute of technology, Chiba (JP)
PCT Filed Mar. 13, 2020, PCT No. PCT/JP2020/011218
§ 371(c)(1), (2) Date Aug. 25, 2022,
PCT Pub. No. WO2021/181686, PCT Pub. Date Sep. 16, 2021.
Prior Publication US 2023/0110609 A1, Apr. 13, 2023
Int. Cl. G01C 21/30 (2006.01); G01C 21/00 (2006.01); G01S 17/08 (2006.01); G01S 17/89 (2020.01); G05D 1/00 (2006.01)
CPC G01C 21/30 (2013.01) [G01C 21/3804 (2020.08); G01S 17/08 (2013.01); G01S 17/89 (2013.01); G05D 1/0248 (2013.01); G05D 1/0274 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A self-localization device that estimates a self-location of a mobile body, the self-localization device comprising:
a detection unit configured to detect a distance to an object around the mobile body as three-dimensional point group data; and
an estimation unit configured to estimate the self-location of the mobile body,
the estimation unit includes:
a map generation unit configured to generate a current map of surroundings of the mobile body based on the three-dimensional point group data detected by the detection unit;
a storage unit configured to store the current map generated by the map generation unit;
a geometric feature extraction unit configured to, based on the current map stored in the storage unit, extract at least one type of geometric feature among three types of geometric features of plane, straight line, and sphere, as a number of current geometric features from the current map, and also as a number of past geometric features from a past map as a map of surroundings of a past location of the mobile body;
a self-location calculation unit configured to select two or three current geometric features from among the number of current geometric features extracted by the geometric feature extraction unit, select a same type and a same number of past geometric features from the number of past geometric features extracted by the geometric feature extraction unit, to form at least one set of geometric features, the self-location calculation unit being configured to calculate at least one self-location in the past map based on the at least one set of geometric features; and
a self-location evaluation unit configured to evaluate a degree of coincidence between the current map and the past map for each set of geometric features of the at least one set of geometric features based on the at least one self-location calculated by the self-location calculation unit, and select a self-location of the at least one self-location with a high degree of coincidence;
wherein the self-location evaluation unit is configured to extract a sphere group from each of the current map and the past map, and evaluates a degree of coincidence between the current map and the past map for each set of geometric features of the at least one set of geometric features based on the sphere group.