TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation

2024-06-03

arXiv.org

AI Summary

To address the challenge of traversability estimation from sparse LiDAR point clouds across diverse environments—including urban and off-road scenes—this paper proposes a lightweight and efficient 3D semantic segmentation network. The core innovation is the TE-NeXt Block, the first to jointly integrate 3D sparse convolution, channel-spatial joint attention, and residual feature fusion, thereby balancing computational efficiency with strong cross-scene modeling capability. The method is trained end-to-end, significantly enhancing generalization and robustness in unstructured environments. It achieves state-of-the-art performance on three major benchmarks—SemanticKITTI, Rellis-3D, and SemanticUSL—with substantial improvements in mean Intersection-over-Union (mIoU). Furthermore, the implementation is fully open-sourced to ensure reproducibility.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Estimate traversability from sparse LiDAR
• Improve 3D sparse convolutional networks
• Enhance generalization in diverse environments

Innovation

• Residual convolutional block design
• Attention mechanisms integration
• 3D sparse convolution techniques