Detection of moving object point clouds by clustering using Depth Map

Abstract

In this paper, we propose a moving object point cloud detection method using point cloud clustering. Conventionally, point clouds obtained with 3D-LiDAR are placed in discretized spaces called occupancy grids, and the static and dynamic environments are estimated by determining whether points exist in each space over time. However, this method misrecognizes parts of the object as partially static, because parts such as the back legs of a pedestrian tend to remain and continue in the occupied grid. Therefore, we overcome the conventional problem by clustering the point clouds obtained with 3D-LiDAR using Depth Map and making static and dynamic judgments for each cluster based on the estimation results of the occupancy grid.