Home // IARIA Congress 2025, The 2025 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications // View article
Authors:
Ikuro Inaga
Masafumi Hashimoto
Kazuhiko Takahashi
Keywords: helmet LiDAR; solid-state LiDAR, SLAM; moving-object tracking; distortion correction; quaternion UKF; micromobility.
Abstract:
This paper presents a Simultaneous Localization And Mapping (SLAM) and Moving Object Tracking (MOT) method using a small and lightweight solid-state Light Detection And Ranging (LiDAR) attached to a rider helmet for micromobilities, such as bicycles, e-bikes, and e-kick scooters. Distortions in LiDAR point cloud data caused by the movement of the micromobility and head motion of the rider are corrected using the data from LiDAR and inertial measurement unit via a quaternion unscented Kalman filter. The corrected LiDAR point cloud data are classified into three classes: 1) point cloud data related to stationary objects, such as buildings and trees, 2) those related to road obstacles, such as curb stones and road debris, and 3) those related to moving objects. The point cloud data related to stationary objects and road obstacles are used for environment mapping using normal distributions transform SLAM, whereas the point cloud data related to moving objects are used for MOT using Kalman filter. Results from experiments conducted at our university campus demonstrate the effectiveness of the proposed method.
Pages: 67 to 73
Copyright: Copyright (c) IARIA, 2025
Publication date: July 6, 2025
Published in: conference
ISBN: 978-1-68558-284-5
Location: Venice, Italy
Dates: from July 6, 2025 to July 10, 2025