The high cost of damaging an expensive robot or injuring people or equipment in its environment make even rare failures unacceptable in many mobile robot applications. Often the objects that pose the highest risk for a mobile robot are those that were not present throughout previous successful traversals of an environment. Change detection, a closely related problem to novelty detection, is therefore of high importance to many mobile robotic applications that require a robot to operate repeatedly in the same environment. We present a novel algorithm for performing online change detection based on a previously developed robust online novelty detection system that uses a learned lower-dimensional representation of the feature space to perform measures of similarity.We then further improve this change detection system by incorporating online scene segmentation to better utilize contextual information in the environment. We validate these approaches through extensive experiments onboard a large outdoor mobile robot. Our results show that our approaches are robust to noisy sensor data and moderate registration errors and maintain their performance across diverse natural environments and conditions.
['Bradford Neuman', 'Boris Sofman', 'Anthony Stentz', 'J. Andrew Bagnell']
Segmentation-based online change detection for mobile robots