Home // IARIA Congress 2023, The 2023 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications // View article

SLAM-based Mapping in Truck-and-Robot System for Last-Mile Delivery Automation

Authors:
Ryo Nakamura
Masafumi Hashimoto
Kazuhiko Takahashi

Keywords: LiDAR; NDT Graph SLAM; map building; loop detection; quadruped robot; delivery automation.

Abstract:
This paper presents a Simultaneous Localization And Mapping (SLAM)-based mapping method for last-mile delivery automation using a scanning Light Detection And Ranging sensor (LiDAR) mounted on a quadruped robot. Distortion in scan data from the LiDAR, caused by the swinging motion of the robot, is corrected by estimating the robot’s pose (three-dimensional positions and attitude angles) in a period shorter than the LiDAR scan period using an extended Kalman filter. LiDAR-scan data related to stationary objects are detected from the corrected scan data using an occupancy grid method. Local maps in small areas where robots deliver goods to customers are built using normal distributions transforms and Graph SLAM. A feature-based loop detection is also performed using surface features and point feature histograms. The local maps are corrected in the Graph SLAM framework using the scan data from LiDAR mounted on a truck stopping at robot depots. Experimental results obtained in our university campus demonstrate the effectiveness of the presented method.

Pages: 31 to 37

Copyright: Copyright (c) IARIA, 2023

Publication date: November 13, 2023

Published in: conference

ISBN: 978-1-68558-089-6

Location: Valencia, Spain

Dates: from November 13, 2023 to November 17, 2023