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Communication Dans Un Congrès Année : 2020

Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes


Real-time dense 3D localization and mapping systems are required to enable robotics platforms to interact in and with their environments. Several solutions have used surfel representations to model the world. While they produce impressive results, they require heavy and costly hardware to operate properly. Many of them are also limited to static environments and small inter-frame motions.Whereas most of the state of the art approaches focus on the accuracy of the reconstruction, we assume that many robotics applications do not require a high resolution level in the rebuilt surface and can benefit from a less accurate but less expensive map, so as to gain in run-time and memory efficiency. In this paper we propose a fast RGB-D SLAM articulated around a rough and lightweight 3D representation for dense compact mapping in dynamic indoor environment, targeting mainstream computing platforms.A simple and fast formulation to detect and filter out dynamic elements is also presented.We show the robustness of our system, its low memory requirement and the good performance it enables.
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hal-03143986 , version 1 (17-02-2021)



Bruce Canovas, Michèle Rombaut, Amaury Nègre, Denis Pellerin, Serge Olympieff. Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes. IROS 2020 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas, United States. pp.4996-5001, ⟨10.1109/IROS45743.2020.9341542⟩. ⟨hal-03143986⟩
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