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ThinkMind // SENSORDEVICES 2022, The Thirteenth International Conference on Sensor Device Technologies and Applications // View article sensordevices_2022_1_70_20029
Simultaneous Localization, Mapping and Moving-Object Tracking Using Helmet-Mounted LiDAR for Micro-Mobility
 Authors:
Ibuki Yoshida
Akihiko Yoshida
Masafumi Hashimoto
Kazuhiko Takahashi Keywords: helmet LiDAR; SLAM; moving-object tracking; micro-mobility. Abstract:
This paper presents a method of Simultaneous Localization, Mapping and Tracking of Moving Objects (SLAMTMO) using a Light Detection And Ranging sensor (LiDAR) mounted on a smart helmet worn by a vehicle rider. This technology can be used to active safety for micro-mobility, such as bicycles, e-bikes, and electric scooters, which are prioritized as personal commuters in the endemic society of coronavirus disease 2019. Distortion in the scan data from the LiDAR is corrected by estimating the helmet’s pose (three-dimensional position and attitude angle) based on the information from Normal Distributions Transform (NDT)-based SLAM and an inertial measurement unit. The static and moving-object scan data, which originate from static and moving objects in the environments, respectively, are classified by subtracting the environment map generated by NDT-based SLAM from the LiDAR current scan data. The moving scan data are used for TMO based on a Bayesian filter, whereas the static scan data are used for point-cloud mapping. The experimental results in a road environment of our university campus show the effectiveness of the proposed SLAMTMO method. Pages: 25 to 31 Copyright: Copyright (c) IARIA, 2022 Publication date: October 16, 2022 Published in: conference ISSN: 2308-3514 ISBN: 978-1-68558-006-3 Location: Lisbon, Portugal Dates: from October 16, 2022 to October 20, 2022
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