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LPG-SLAM: a Light-weight Probabilistic Graph-based SLAM

Abstract : Most of Current autonomous navigation solutions critically rely on SLAM systems for localisation, especially in GPS-denied environments but also in urban and indoor environments. Their efficacy and efficiency thus depend on the ability of the underlying SLAM method to map large-scale environments in a data-efficient manner. State of the art systems, while accurate, often require powerful hardware such as GPUs, and need careful tuning of hyperparameters in order to adapt to the user's needs. In this paper, we propose a lightweight but accurate probabilistic 2D graph-based SLAM system. We validate our approach on sequences from the KITTI dataset as well as on data gathered by our experimental platform.
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Contributor : Kathia Melbouci Connect in order to contact the contributor
Submitted on : Friday, December 18, 2020 - 3:37:43 PM
Last modification on : Friday, January 21, 2022 - 3:18:40 AM
Long-term archiving on: : Friday, March 19, 2021 - 8:40:31 PM



  • HAL Id : hal-03081646, version 1



Kathia Melbouci, Fawzi Nashashibi. LPG-SLAM: a Light-weight Probabilistic Graph-based SLAM. ICARCV 2020 - International Conference on Control, Automation, Robotics and Vision, Nanyang Technological University; Zhijiang University; Shenzhen Polytechnic, Dec 2020, Shenzhen / Virtual, China. ⟨hal-03081646⟩



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