US 11,703,596 B2
Method and system for automatically processing point cloud based on reinforcement learning
Seongjoo Moon, Yongin-si (KR)
Assigned to HL KLEMOVE CORP., Incheon (KR)
Filed by HL KLEMOVE CORP., Incheon (KR)
Filed on Mar. 30, 2021, as Appl. No. 17/217,338.
Claims priority of application No. 10-2020-0038695 (KR), filed on Mar. 31, 2020.
Prior Publication US 2021/0304496 A1, Sep. 30, 2021
Int. Cl. G01S 17/86 (2020.01); G01S 17/89 (2020.01); G06N 20/00 (2019.01); G06T 17/20 (2006.01); G06T 7/50 (2017.01); G06T 7/80 (2017.01); G06T 7/70 (2017.01); G01S 7/48 (2006.01); G01S 17/42 (2006.01); G06F 18/22 (2023.01); G06F 18/214 (2023.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01)
CPC G01S 17/86 (2020.01) [G01S 7/4802 (2013.01); G01S 17/42 (2013.01); G01S 17/89 (2013.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 20/00 (2019.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06T 7/80 (2017.01); G06T 17/20 (2013.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A method for automatically processing point cloud based on reinforcement learning, comprising:
scanning to collect a point cloud (PCL) and an image through a Light Detection and Ranging (Lidar) and a camera;
calibrating, by a controller, to match locations of the image and the point cloud through reinforcement learning that maximizes a reward including geometric and luminous intensity consistency of the image and the point cloud; and
meshing, by the controller, the point cloud into a 3D image through reinforcement learning that minimizes a reward including a difference between a shape of the image and a shape of the point cloud,
wherein the meshing is performed by an action including a motion vector of a catch particle, and
wherein the meshing comprises determining the motion vector to capture a point cloud having highest correlation among adjacent point clouds.