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.

TE-NeXt 是一种基于残差卷积块的新颖高效的架构，能够从稀疏的 LiDAR 点云中进行可通行性估计（TE）。通过融合当前趋势的注意机制和 3D 稀疏卷积的概念，TE-NeXt 旨在展示其在各种城市和自然环境下的泛化能力，使用了如 SemanticKITTI、Rellis-3D 和 SemanticUSL 等公认和可获取的数据集，并超越了现有技术方法，在语义分割问题上展现出更好的结果，对于非结构化环境保持高可靠性和鲁棒性，在城市环境中有更好的抽象表现。实现代码已在开放资源库中提供，以确保结果的可重复性。