US 12,216,467 B2
Self-location estimation device, autonomous mobile body, and self-location estimation method
Shin Yoshimura, Kanagawa (JP)
Assigned to SONY SEMICONDUCTOR SOLUTIONS CORPORATION, Kanagawa (JP)
Appl. No. 17/754,107
Filed by SONY SEMICONDUCTOR SOLUTIONS CORPORATION, Kanagawa (JP)
PCT Filed Sep. 23, 2020, PCT No. PCT/JP2020/035687
§ 371(c)(1), (2) Date Mar. 24, 2022,
PCT Pub. No. WO2021/065594, PCT Pub. Date Apr. 8, 2021.
Claims priority of application No. 2019-181183 (JP), filed on Oct. 1, 2019.
Prior Publication US 2022/0291686 A1, Sep. 15, 2022
Int. Cl. G01C 21/34 (2006.01); G01C 21/30 (2006.01); G05D 1/00 (2006.01); G06T 7/73 (2017.01)
CPC G05D 1/0088 (2013.01) [G01C 21/30 (2013.01); G05D 1/0246 (2013.01); G05D 1/0272 (2013.01); G05D 1/0274 (2013.01); G06T 7/74 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/30252 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A self-location estimation device, comprising:
a central processing unit (CPU) configured to:
receive current image information from one or more image sensors of an autonomous mobile body, wherein the autonomous mobile body is movable in a real space;
estimate a current first self-location of the autonomous mobile body based on the current image information and environmental map information, wherein
the environmental map information includes a plurality of pairs of past image information acquired in past by the one or more image sensors, feature data of the plurality of pairs of the past image information, and a past self-location of the autonomous mobile body at a time of the acquisition of each of the plurality of pairs of the past image information,
the plurality of pairs of the past image information, the feature data, and the past self-location of each of the plurality of pairs of the past image information are associated on a map in the environmental map information, and
the feature data corresponds to feature points of the plurality of pairs of the past image information;
estimate a current second self-location of the autonomous mobile body based on the current image information and a learned parameter, wherein the learned parameter is based on the environmental map information; and
integrate the current first self-location and the current second self-location to estimate a third current self-location of the autonomous mobile body.