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This is a Quantitative Study study.

October 2, 2025

A real-time LiDAR-based terrain traversability classification approach in off-road environments

Key Points

The proposed method achieves high accuracy in classifying terrain traversability in off-road environments.
With an inference speed exceeding 110 FPS, efficient decision-making capabilities are demonstrated using LiDAR.
The network features advanced dynamic voxelization and point cloud processing techniques for effective feature extraction.
Tests conducted on datasets such as RELLIS-3D highlight the method's effectiveness for autonomous vehicle navigation.

Abstract

Abstract Terrain traversability analysis is essential for realizing autonomous navigation. This paper proposes a real-time light detection and ranging (LiDAR)-based network for terrain traversability classification in off-road environments. This network incorporates a fast BEV (Bird’s Eye View) feature map generation module, which performs dynamic voxelization, pillar feature encoding and scatter on point cloud, and a traversability completion module that generates accurate and dense BEV traversability maps. The network is trained with dense ground truth labels generated through offline data processing, enabling accurate and dense traversability classification of the surrounding terrain centered on the ego vehicle, with an inference speed reaching 110 + FPS. Finally, we conduct qualitative and quantitative experiments on the RELLIS-3D off-road dataset and SemanticKITTI on-road dataset, which demonstrate the efficiency and accuracy of the proposed approach.

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