Online novelty-based visual obstacle detection for field robotics

This paper presents a novel online unsupervised vision system for obstacle detection in field environments which detects many obstacles pathological to appearance- or structure-only obstacle detection systems. Robust obstacle detection in field environments is challenging as it is infeasible to train on all possible obstacles in all conditions, and many obstacles are camouflaged in their appearance or structure. The proposed system combines novelty in structure and appearance cues to detect obstacles, can adapt over time to changes in the environment, and is suitable for long-term operation over changing lighting conditions in various environments. After an initial learning period the method exhibits very few false positives, while successfully detecting most obstacles over both daytime and nighttime datasets including challenging obstacles such as a person lying down in grass.