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 |
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.
|