Simultaneous Object Tracking and Shape Reproduction Using LiDAR Point Cloud Data.

In this study, we use light detection and ranging (LiDAR) to track moving objects and estimate their positions, velocities, and shapes. Both the position and shape of a target moving object are estimated based on the time-series point cloud for the object using simultaneous localization and mapping (SLAM), which estimates the self-position and the surrounding environment. After point cloud data for dynamic objects (e.g., people and vehicles) are extracted, the center of gravity of each dynamic object point cloud is tracked using a joint probabilistic data association filter (JPDAF) to obtain the point cloud for the target. It is shown that it is possible to estimate the position, velocity, and shape of moving objects based on LiDAR data.