Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

Abstract

This paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the original raw data. Different filtering configurations were tested: raw (original point cloud), dynamic (dynamic obstacles are removed from the point cloud), dynamic vehicles (vehicles are removed), far (distant points are removed), ground (the points belonging to the ground are removed) and structure (only structures and objects are kept in the point cloud). The experiments were performed using the KITTI and SemanticKITTI datasets, which feature different scenarios that allowed identifying the implications and relevance of each element of the environment in LiDAR odometry algorithms. The conclusions obtained from this work are of special relevance for improving the efficiency of LiDAR odometry algorithms in all kinds of scenarios.