Analysis on 2D mapping for mobile robot on the sharp edges area

Mohamad Heerwan, Peeie and Desmond Ling, Ze Yew and Kettner, Maurice and Muhammad Aizzat, Zakaria (2024) Analysis on 2D mapping for mobile robot on the sharp edges area. In: Proceedings of the 9th International Conference on Mechatronics Engineering, ICOM 2024. 9th International Conference on Mechatronics Engineering, ICOM 2024 , 13 - 14 August 2024 , IIUM Gombak. pp. 255-263.. ISBN 979-8-3503-4978-8 (Published)

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Abstract

Simultaneous localization and mapping (SLAM) is a fundamental technique block in the indoor navigation system for most autonomous vehicles and robots. One of the issues in SLAM is that the speed of the robot may affect the mapping quality. Therefore, LiDAR self-motion distortion is a common challenge for different SLAM algorithms, especially in environments with sharp edges. Due to this issue, this study aims to analyze the impact of LiDAR self-motion distortions on three SLAM algorithms: GMapping, Hector SLAM, and Google Cartographer. These algorithms are implemented on a TurtleBot3 Burger robot to perform 2D mapping under different speed conditions (0.07m/s, 0.14m/s, and 0.22m/s) in the Control System Lab at U niversiti Malaysia Pahang AI- Sultan Abdullah (UMPSA). The quality of the generated maps is evaluated by measuring the length of predefined walls and the angle of predefined corners and comparing them with the actual dimensions in the real world. The absolute error and statistical error metrics (MAE, MSE, RMSE, and MAPE) are computed for each data point and each algorithm. The results show that Hector SLAM is the most robust algorithm under high speed, all the walls and corners can be accurately mapped, with the lowest MAPE value, due to its independence of odometry data. The results also reveal that the effect of LiDAR self-motion distortion increases with speed, as indicated by the higher error values for all the algorithms. This study contributes to the understanding of how LiDAR self-motion distortions affect the performance of different SLAM algorithms and provides insights for choosing the appropriate algorithm for different speed scenarios.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Autonomous robot; Localization; mapping; Self-motion distortion; Sharp edges
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology Faculty of Mechanical and Automotive Engineering Technology
Depositing User:	Dr. Mohamad Heerwan Peeie
Date Deposited:	24 Feb 2025 03:35
Last Modified:	24 Feb 2025 03:35
URI:	http://umpir.ump.edu.my/id/eprint/43890
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