A Framework for Real-time Generation of Multi-directional Traversability Maps in Unstructured Environments

In complex unstructured environments, accurate terrain traversability analysis is a fundamental requirement for the successful execution of any movements of ground robots, especially given that terrain traversability often exhibits anisotropy. However, the difficulty in obtaining multi-directional terrain labels hinders the emergence of end-to-end multi-directional traversability network. This paper introduces a framework for real-time multi-directional traversability maps (MTraMap) generation tailored for unstructured environments. It involves pre-training a uni-directional traversability classifier, termed UniTraT, through self-supervised learning using ground robot travel simulation. Furthermore, it employs Uni-directional to Multi-directional Traversability Distillation (UMTraDistill) to distill a multi-directional traversability network, termed MultiTCNN, which is capable of directly generating MTraMap. We evaluated both networks on our traversability dataset, achieving an 89% accuracy in terrain traversability classification with the UniTraT. Compared to UniTraT, the accuracy of the MultiTCNN distilled via UMTraDistill only decreases by 1.8%, and it can process 10 m x 10 m elevation map at a speed of 74 fps. Field robotics experiments were also conducted and showed that MultiTCNN can generate MTraMap of the surrounding 20 m x 20 m environment at a rate of 9.39 fps, with a slight reduction of 0.61 fps compared to the lidar data publishing rate, and the generated MTraMap can clearly delineate the multi-directional traversability of the surrounding environments.