This paper presents TE-NeXt, a novel and efficient architecture for Traversability Estimation (TE) from sparse LiDAR point clouds based on a residual convolution block. TE-NeXt block fuses notions of current trends such as attention mechanisms and 3D sparse convolutions. TE-NeXt aims to demonstrate high capacity for generalisation in a variety of urban and natural environments, using well-known and accessible datasets such as SemanticKITTI, Rellis-3D and SemanticUSL. Thus, the designed architecture ouperforms state-of-the-art methods in the problem of semantic segmentation, demonstrating better results in unstructured environments and maintaining high reliability and robustness in urbans environments, which leads to better abstraction. Implementation is available in a open repository to the scientific community with the aim of ensuring the reproducibility of results.

翻译：本文提出了TE-NeXt，一种基于残差卷积块、从稀疏LiDAR点云进行可通行性估计（TE）的新型高效架构。TE-NeXt块融合了注意力机制和三维稀疏卷积等当前主流技术思想。TE-NeXt旨在利用如SemanticKITTI、Rellis-3D和SemanticUSL等知名公开数据集，证明其在多种城市与自然环境中的强大泛化能力。因此，所设计的架构在语义分割问题上超越了现有最优方法，在非结构化环境中表现出更优性能，并在城市环境中保持了高可靠性与鲁棒性，从而实现了更好的特征抽象。为实现结果可复现，相关实现代码已在开源平台向科学社区公开。