  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.
