Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation

摘要

Autonomous mobile robot navigation, either off-road or on ill-structured roads, presents unique challenges for machine perception. A successful terrain or roadway classifier must be able to learn in a self-supervised manner and adapt to inter- and intra-run changes in the local environment.