TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation
This work addresses traversability estimation for autonomous navigation in varied environments, representing an incremental improvement by combining existing techniques like attention mechanisms and 3D sparse convolutions.
The paper tackles traversability estimation from sparse LiDAR point clouds by proposing TE-NeXt, a novel architecture that outperforms state-of-the-art methods in semantic segmentation, showing better results in unstructured environments and maintaining high reliability in urban settings.
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.