Abstract
Simultaneous localization and mapping (SLAM) algorithms using data from LiDAR sensors, which provide accurate distance measurements, regardless of the lighting condition, are widely studied and used. Carrying out mapping in simulated environments can be used to analyze the resources used in mapping and planning the trajectory necessary for better acquisition of information from the real environment. In this work, three odometry and mapping techniques using a LiDAR type sensor were compared: LeGO-LOAM, A-LOAM and F-LOAM, with the odometry obtained by Ground Truth and the extended kalman filter (EKF). The LiDAR SLAM algorithms performed well compared to filtered odometry, especially in environments with more features.
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Acknowledgement
The authors acknowledge the financial suppport provided by CNPq grant number [311029/2020-5 and 407163/2022-0], and the CAPES - Finance Code 001.
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de Carvalho, M.E.C., Ribeiro, T.T., Conceicao, A.G.S. (2024). Comparative Analysis of LiDAR SLAM Techniques in Simulated Environments in ROS Gazebo. In: Youssef, E.S.E., Tokhi, M.O., Silva, M.F., Rincon, L.M. (eds) Synergetic Cooperation between Robots and Humans. CLAWAR 2023. Lecture Notes in Networks and Systems, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-031-47272-5_23
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DOI: https://doi.org/10.1007/978-3-031-47272-5_23
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