TA-TOS: Terrain-Aware Tiny Obstacle Segmentation Based on MRF Road Modeling Using 3-D LiDAR Scans

Robust obstacle segmentation remains critical for the safety of intelligent transportation systems (ITS), where LiDAR-based perception systems form the cornerstone of vehicle-environment interaction. Although state-of-the-art (SOTA) LiDAR-based approaches have demonstrated high performance in segmenting common obstacles, the results for tiny obstacle segmentation are still unsatisfactory. However, such tiny obstacles, e.g., curbs, gravel, and potholes, pose significant threats to ground vehicles, undermining ITS operational safety and surface transportation traffic efficiency. It is challenging for SOTA methods to distinguish tiny obstacles due to their inability to precisely model road surfaces, particularly bumpy road surfaces. To address this problem, this paper proposes a road modeling method based on the Markov random field (MRF), possessing stronger road surface modeling capability. A novel negative exponential energy function is introduced to simultaneously ensure the smoothness of the road model and the consistency with the road undulation. After the energy minimization of the MRF, the segmentation of obstacles (including both positive and negative obstacles) is achieved by computing the signed distance to the refined road model. Our proposed terrain-aware tiny obstacle segmentation (TA-TOS) method is compatible with different terrains and different LiDARs, without any prior data or pre-training. We evaluate the performance of TA-TOS on the SemanticKITTI dataset, and two self-built datasets containing tiny obstacles from actual urban mobility systems (road scenarios) and mining haulage systems (off-road scenarios), respectively. Our proposed TA-TOS method achieves much better performance than the SOTA LiDAR-based segmentation approaches, particularly on roads with pronounced undulation. The results show that the improvement is more significant for the segmentation of smaller obstacles. Our source code is publicly available at github.com/ryming2001/TA-TOS.