自律走行オフロードナビゲーションのための学習ベース走行可能コストマップ
Learning-based Traversability Costmap for Autonomous Off-road Navigation
視覚と幾何情報を組み合わせ、固有感覚情報を用いたリスク考慮型ラベリングでオフロード走行可能コストマップを学習し、安全で滑らかなナビゲーションを実現した。
著者: Qiumin Zhu, Zhen Sun, Songpengcheng Xia, Guoqing Liu, Kehui Ma, Ling Pei, Zheng Gong, Cheng Jin
分類: cs.RO
原文アブストラクト
Traversability estimation in off-road terrains is an essential procedure for autonomous navigation. However, creating reliable labels for complex interactions between the robot and the surface is still a challenging problem in learning-based costmap generation. To address this, we propose a method that predicts traversability costmaps by leveraging both visual and geometric information of the environment. To quantify the surface properties like roughness and bumpiness, we introduce a novel way of risk-aware labelling with proprioceptive information for network training. We validate our method in costmap prediction and navigation tasks for complex off-road scenarios. Our results demonstrate that our costmap prediction method excels in terms of average accuracy and MSE. The navigation results indicate that using our learned costmaps leads to safer and smoother driving, outperforming previous methods in terms of the highest success rate, lowest normalized trajectory length, lowest time cost, and highest mean stability across two scenarios.
