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Affiliation

University Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche (Alicante), Spain

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1 Papers

Computing the Traversability of the Environment by Means of Sparse Convolutional 3D Neural Networks

Topics: Mobile Robots and Autonomous Systems; Perception and Awareness; Guidance, Navigation and Control

Authors: Antonio Santo ^{1

;

2} , Arturo Gil ¹ , David Valiente ¹ , Mónica Ballesta ¹ and Adrián Peidró ¹

Affiliations: ¹ University Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche (Alicante), Spain , ² Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI), Camí de Vera S/N, Edificio 3Q, 46022 Valencia, Spain

Digital Object Identifier: 10.5220/0012160300003543

Proceedings of ICINCO 2023, ISBN: 978-989-758-670-5

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The correct assessment of the environment in terms of traversability is strictly necessary during the navigation task in autonomous mobile robots. In particular, navigating along unknown, natural and unstructured environments requires techniques to select which areas can be traversed by the robot. In order to increase the autonomy of the system’s decisions, this paper proposes a method for the evaluation of 3D point clouds obtained by a LiDAR sensor in order to obtain the transitable areas, both in road and natural environments. Specifically, a trained sparse encoder-decoder configuration with rotation invariant features is proposed to replicate the input data by associating to each point the learned traversability features. Experimental results show the robustness and effectiveness of the proposed method in outdoor environments, improving the results of other approaches.