Deep Evidential Uncertainty Estimation for Semantic Segmentation under Out-Of-Distribution Obstacles
Siddharth Ancha,Philip R. Osteen,Nicholas Roy,Siddharth Ancha,Philip R. Osteen,Nicholas Roy
In order to navigate safely and reliably in novel environments, robots must estimate perceptual uncertainty when confronted with out-of-distribution (OOD) obstacles not seen in training data. We present a method to accurately estimate pixel-wise uncertainty in semantic segmentation without requiring real or synthetic OOD examples at training time. From a shared per-pixel latent feature representat...
