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Processing of LiDAR and IMU Data for Target Detection and Odometry of a Mobile Robot Cover

Processing of LiDAR and IMU Data for Target Detection and Odometry of a Mobile Robot

Open Access
|Apr 2023

Abstract

In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is demonstrated, introducing an innovative methodology to manage the data and extract useful information. The LiDAR sensor is placed on a mobile robot which has a modular design that permits the easy change of the number of wheels, was designed to travel through several environments, and saves energy by changing the number and arrangement of the wheels in each environment. In addition, the robot can recognize landmarks in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterize the robot’s pose by the surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analyzed, highlighting new improvements in the future prospective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.

DOI: https://doi.org/10.14313/jamris/1-2022/1 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
Language: English
Page range: 3 - 13
Submitted on: Jun 10, 2021
Accepted on: Aug 2, 2022
Published on: Apr 4, 2023
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Nicola Ivan Giannoccaro, Takeshi Nishida, Aimè Lay-Ekuakille, Ramiro Velazquez, Paolo Visconti, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.