Uncertainty Estimation for Planetary Robotic Terrain Segmentation

Terrain Segmentation information is crucial input for current and future planetary robotic missions. Labeling training data for terrain segmentation is a difficult task and can often cause semantic ambiguity. As a result, large portion of an image usually remains unlabeled. Therefore, it is difficult to evaluate network performance on such regions. Worse is the problem of using such a network for inference, since the quality of predictions cannot be guaranteed if trained with a standard semantic segmentation network. This can be very dangerous for real autonomous robotic missions since the network could predict any of the classes in a particular region, and the robot does not know how much of the prediction to trust. To overcome this issue, we investigate the benefits of uncertainty estimation for terrain segmentation. Knowing how certain the network is about its prediction is an important element for a robust autonomous navigation. In this paper, we present neural networks, which not only give a terrain segmentation prediction, but also an uncertainty estimation. We compare the different methods on the publicly released real world Mars data from the MSL mission.