Exploring the Effect of 3D Object Removal using Deep Learning for LiDAR-based Mapping and Long-term Vehicular Localization

Abstract

The construction of a 3D point cloud map for long-term vehicle localization has always been a challenge in the autonomous driving community. In a highly dynamic urban environment, Simultaneous Localization and Mapping (SLAM) systems can be heavily affected by non-permanent objects and features during the mapping process, leaving undesired noise in the final map, which is redundant for long-term application. This paper explores applying object detection neural networks in SLAM systems to reduce the effect from dynamic objects (e.g., vehicles, pedestrians, cyclists). Specifically, we use Point-Pillars to detect and remove objects first and then perform LOAM to achieve a more static mapping. In the experimental section, the proposed method is validated on the public KITTI Odometry dataset. Compared with the original LOAM, the proposed method can provide a static point cloud map, thus improving the robustness of long-term vehicle applications.