RT Journal Article
T1 Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
A1 Moreno Olivo, Francisco Miguel
A1 Guindel Gómez, Carlos
A1 Armingol Moreno, José María
A1 García Fernández, Fernando
AB 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.
PB MDPI
SN 2076-3417
YR 2020
FD 2020-08-02
LK https://hdl.handle.net/10016/33158
UL https://hdl.handle.net/10016/33158
LA eng
NO This article belongs to the Special Issue Intelligent Transportation Systems
NO Research was supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and PEAVAUTO-CM-UC3M.
DS e-Archivo
RD 1 sept. 2024