Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments

Discriminating terrain traversability stands as a pivotal challenge for autonomous driving in off-road environments. The complexity arises from the diverse and ambiguous nature of off-road conditions, coupled with the specific characteristics of the driving platform. To address this challenge, we introduce a novel self-supervised learning framework for terrain traversability analysis, incorporating a contrastive label disambiguation mechanism. The proposed framework integrates traversability learning with real-time scene reconstruction. By projecting actual driving experience onto the terrain models, weakly labeled training samples with pseudo-labels can be automatically generated. Furthermore, a prototype-based contrastive representation learning method with the aid of a local window-based transformer encoder is designed to learn distinguishable embeddings, facilitating the self-supervised updating of those pseudo labels. Through the iterative interaction between representation learning and pseudo label updating, the inherent ambiguities associated with those pseudo labels are gradually eliminated. This enables the acquisition of fine-grained and platform-specific terrain traversability insights, eliminating the need for any human-provided annotations. Experimental results on the publicly available RELLIS-3D dataset and two self-collected datasets demonstrate the effectiveness of the proposed method.