UNRealNet: 実環境の高精度スキャンから不確実性を考慮したナビゲーション特徴を学習
UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments
低品質なLiDARスキャンから高精度な走行可能領域特徴を予測する不確実性対応ネットワークを提案し、脚ロボットでの有効性を示した。
著者: Samuel Triest, David D. Fan, Sebastian Scherer, Ali-Akbar Agha-Mohammadi
分類: cs.RO
原文アブストラクト
Traversability estimation in rugged, unstructured environments remains a challenging problem in field robotics. Often, the need for precise, accurate traversability estimation is in direct opposition to the limited sensing and compute capability present on affordable, small-scale mobile robots. To address this issue, we present a novel method to learn [u]ncertainty-aware [n]avigation features from high-fidelity scans of [real]-world environments (UNRealNet). This network can be deployed on-robot to predict these high-fidelity features using input from lower-quality sensors. UNRealNet predicts dense, metric-space features directly from single-frame lidar scans, thus reducing the effects of occlusion and odometry error. Our approach is label-free, and is able to produce traversability estimates that are robot-agnostic. Additionally, we can leverage UNRealNet's predictive uncertainty to both produce risk-aware traversability estimates, and refine our feature predictions over time. We find that our method outperforms traditional local mapping and inpainting baselines by up to 40%, and demonstrate its efficacy on multiple legged platforms.
