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Safe Robot Navigation via Multi-Modal Anomaly Detection

Presentation at ICRA 2020: Safe robot navigation in the presence of unknown obstacles using anomaly detection. Paper: https://doi.org/10.3929/ethz-b-000392927 Code & Dataset: https://github.com/leggedrobotics/ano... Video & Presentation by Lorenz Wellhausen Paper Abstract: 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.