自己教師ありコストマップ学習によるオフロード走行の走破性予測
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
外界センサ情報と自己受容感覚による地形との相互作用フィードバックを自己教師ありで組み合わせ、オフロード環境での走破性コストマップを予測する手法を提案。ロボットの速度も考慮し、大規模なナビゲーション実験で介入回数を最大57%削減した。
著者: Mateo Guaman Castro, Samuel Triest, Wenshan Wang, Jason M. Gregory, Felix Sanchez, John G. Rogers, Sebastian Scherer
分類: cs.RO, cs.LG
原文アブストラクト
Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to create informative labels to learn a model in a supervised manner for these interactions. We propose a method that learns to predict traversability costmaps by combining exteroceptive environmental information with proprioceptive terrain interaction feedback in a self-supervised manner. Additionally, we propose a novel way of incorporating robot velocity in the costmap prediction pipeline. We validate our method in multiple short and large-scale navigation tasks on challenging off-road terrains using two different large, all-terrain robots. Our short-scale navigation results show that using our learned costmaps leads to overall smoother navigation, and provides the robot with a more fine-grained understanding of the robot-terrain interactions. Our large-scale navigation trials show that we can reduce the number of interventions by up to 57% compared to an occupancy-based navigation baseline in challenging off-road courses ranging from 400 m to 3150 m. Appendix and full experiment videos can be found in our website: https://mateoguaman.github.io/hdif.
