マルチモーダル異常検知による安全なロボットナビゲーション
Safe Robot Navigation via Multi-Modal Anomaly Detection
RGB・深度・表面法線を組み合わせた異常検知で走行可能領域を分類し、未知環境でも過信せず安全にナビゲーションできる手法を提案・評価した。
著者: Lorenz Wellhausen, René Ranftl, Marco Hutter
分類: cs.RO
原文アブストラクト
Navigation in natural outdoor environments requires a robust and reliable traversability classification method to handle the plethora of situations a robot can encounter. Binary classification algorithms perform well in their native domain but tend to provide overconfident predictions when presented with out-of-distribution samples, which can lead to catastrophic failure when navigating unknown environments. We propose to overcome this issue by using anomaly detection on multi-modal images for traversability classification, which is easily scalable by training in a self-supervised fashion from robot experience. In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. Our results show that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best. It achieves over 95% area under the ROC curve and is robust to out-of-distribution samples.
