Computing the Traversability of the Environment by Means of Sparse Convolutional 3D Neural Networks
Antonio Santo, Arturo Gil, David Valiente, Mónica Ballesta, Adrián Peidró
Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics  (Roma, 13-15 de noviembre de 2023)
Ed. Scitepress  ISBN:978-989-758-670-5  ISSN:2184-2809  DOI:10.5220/0000168300003543  - Vol. 1, pags. 383-393


The correct assessment of the environment in terms of traversability is strictly necessary during the naviga-

tion 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.