Safe Robot Navigation via Multi-Modal Anomaly Detection

Lorenz Wellhausen, Rene Ranftl, Marco Hutter
2020 IEEE Robotics and Automation Letters  
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
more » ... raversability 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.
doi:10.1109/lra.2020.2967706 fatcat:7t5acmueorff7flufz6wvmtnru