Navigating off-road with a fast autonomous vehicle depends on a robust
perception system that differentiates traversable from non-traversable terrain.
Typically, this depends on a semantic understanding which is based on
supervised learning from images annotated by a human expert. This requires a
significant investment in human time, assumes correct expert classification,
and small details can lead to misclassification. To address these challenges,
we propose a method for predicting high- and low-risk terrains from only past
vehicle experience in a self-supervised fashion. First, we develop a tool that
projects the vehicle trajectory into the front camera image. Second, occlusions
in the 3D representation of the terrain are filtered out. Third, an autoencoder
trained on masked vehicle trajectory regions identifies low- and high-risk
terrains based on the reconstruction error. We evaluated our approach with two
models and different bottleneck sizes with two different training and testing
sites with a fourwheeled off-road vehicle. Comparison with two independent test
sets of semantic labels from similar terrain as training sites demonstrates the
ability to separate the ground as low-risk and the vegetation as high-risk with
81.1% and 85.1% accuracy
