Probabilistic graph-based real-time ground segmentation for urban robotics

Abstract

© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License

Terrain analysis is of paramount importance for the safe navigation of autonomous robots. In this study, we introduce GATA, a probabilistic real-time graph-based method for segmentation and traversability analysis of point clouds. In the method, we iteratively refine the parameters of a ground plane model and identify regions imaged by a LiDAR as traversable and non-traversable. The method excels in delivering rapid, high-precision obstacle detection, surpassing existing state-of-the-art methods. Furthermore, our method addresses the need to distinguish between surfaces with varying traversability, such as vegetation or unpaved roads, depending on the specific application. To achieve this, we integrate a shallow neural network, which operates on features extracted from the ground model. This enhancement not only boosts performance but also maintains real-time efficiency, without the need for GPUs. The method is rigorously evaluated using the SemanticKitti dataset and its practicality is showcased through real-world experiments with an urban last-mile delivery autonomous robot. The code is publicly available at https://gitlab.iri.upc.edu/idelpino/iri_ground_segmentation

This work was supported in part by the EIT Urban Mobility project LOGISMILE under Grant EIT-UM-2020-22140 and Grant EIT-UM-2023-23374, in part by the Spanish Projects EBCON under Grant PID2020-119244GB-I00, in part by MCIN/AEI/10.13039/501100011033, AUDEL under Grant TED2021-131759A-I00, in part by MCIN/ AEI/10.13039/501100011033, in part by the “European Union NextGenerationEU/PRTR” and LENA under Grant PID2022-142039NA-I00, in part by MCIN/ AEI/10.13039/501100011033, in part by “ERDF A way of making Europe”, in part by BotNet under Grant 23S06128-00, in part by the “Ajuntament de Barcelona” and “Fundació la Caixa”, in part by the Consolidated Research Group RAIG under Grant 2021 SGR 00510 of the Departament de Recerca i Universitats de la Generalitat de Catalunya, in part by a Margarita Salas Fellowship to IDP (MARSALAS21-08) funded by the Spanish Ministry of Universities, in part by the European Union-Next Generation, and in part by the University of Alicante.

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