Three-dimensional sparse convolutional neural network for terrain traversability analysis and autonomous motion planning
Antonio Santo, Juan J. Cabrera, Carlos Viegas, David Valiente, Arturo Gil
Engineering Applications of Artificial Intelligence  (July 2026)
Ed. Elsevier  DOI:https://doi.org/10.1016/j.engappai.2026.114595  BIBTEX:@article{SANTO2026114595, title = {Three-dimensional sparse convolutional neural network for terrain traversability analysis and autonomous motion planning}, journal = {Engineering Applications of Artificial Intelligence}, volume = {175}, pages = {114595}, year = {2026}, issn = {0952-1976}, doi = {https://doi.org/10.1016/j.engappai.2026.114595}, url = {https://www.sciencedirect.com/science/article/pii/S0952197626008766}, author = {Antonio Santo and Juan J. Cabrera and Carlos Viegas and David Valiente and Arturo Gil}, keywords = {Traversability estimation, Artificial Intelligence, Computer vision, Mobile robotics, Autonomous navigation}, abstract = {Among the multitude of scenarios in which autonomous robots are intended to operate, natural environments present the most significant challenges in the context of traversability estimation compared to structured settings. To address these complexities without compromising urban performance, we propose TE-NeXt (Traversability Estimation Convolutional Network), a customized 3D sparse convolutional architecture tailored for unstructured terrain. This network is a customized and efficient architecture for traversability estimation from sparse LiDAR (Light Detection and Ranging) point clouds based on a encoder–decoder topology that includes several modifications regarding: (i) the input features; (ii) the structure of encoder–decoder resolution levels; and (iii) the constitution of the 3D (three-dimensional) convolutional block. Thus, the experimental results demonstrate superior performance in unstructured terrain (82% F1 score on Rellis-3D), high robustness in urban environments (SemanticKITTI), and strong generalization capabilities in mixed environments (SemanticUSL). Finally, we present a fully autonomous navigation framework utilizing this method and release the source code to ensure reproducibility.} }  - Volume 175

Resumen:

Among the multitude of scenarios in which autonomous robots are intended to operate, natural environments present the most significant challenges in the context of traversability estimation compared to structured settings. To address these complexities without compromising urban performance, we propose TE-NeXt (Traversability Estimation Convolutional Network), a customized 3D sparse convolutional architecture tailored for unstructured terrain. This network is a customized and efficient architecture for traversability estimation from sparse LiDAR (Light Detection and Ranging) point clouds based on a encoder–decoder topology that includes several modifications regarding: (i) the input features; (ii) the structure of encoder–decoder resolution levels; and (iii) the constitution of the 3D (three-dimensional) convolutional block. Thus, the experimental results demonstrate superior performance in unstructured terrain (82% F1 score on Rellis-3D), high robustness in urban environments (SemanticKITTI), and strong generalization capabilities in mixed environments (SemanticUSL). Finally, we present a fully autonomous navigation framework utilizing this method and release the source code to ensure reproducibility.