Home // ALLSENSORS 2020, The Fifth International Conference on Advances in Sensors, Actuators, Metering and Sensing // View article

Normal Distributions Transform-Based Mapping Using Scanning LiDAR Mounted on Motorcycle

Authors:
Kota Matsuo
Akihiko Yoshida
Masafumi Hashimoto
Kazuhiko Takahashi

Keywords: motorcycle; LiDAR; NDT-based SLAM; distortion correction; dynamic environment.

Abstract:
This paper presents a 3D point cloud mapping method for Global Navigation Satellite Systems (GNSS)-denied and dynamic environments using a scanning multilayer Light Detection And Ranging (LiDAR) mounted on a motorcycle. The distortion in the scan data from the LiDAR is corrected by estimating the motorcycle’s pose (3D positions and attitude angles) in a period shorter than the LiDAR scan period based on the information from Normal Distributions Transform (NDT) scan matching and an Inertial Measurement Unit (IMU). The corrected scan data are mapped onto an elevation map. The static and moving scan data, which originate from static and moving objects in the environments, respectively, are classified using the occupancy grid method. Only the static scan data are applied to generate a point cloud map using NDT-based Simultaneous Localization And Mapping (SLAM). The experimental results obtained in an urban road environment demonstrate the qualitative effectiveness of the proposed method.

Pages: 69 to 75

Copyright: Copyright (c) IARIA, 2020

Publication date: March 22, 2020

Published in: conference

ISSN: 2519-836X

ISBN: 978-1-61208-766-5

Location: Valencia, Spain

Dates: from November 21, 2020 to November 21, 2020