Active vision-based robot localization and navigation in a visual memory

We present a new strategy for active vision-based localization and navigation of a mobile robot in a visual memory, i.e., within a previously-visited area represented as a large collection of images. Vision-based localization in such a large and dynamic visual map is intrinsically ambiguous, since more than one map-locations can exhibit the same visual appearance as the current image observed by the robot. Most existing approaches are passive, i.e., they do not devise any strategy to resolve this ambiguity. In this work, we present an active vision-based localization and navigation strategy that can disambiguate the true initial location among possible hypotheses by controlling the mobile observer across a sequence of highly distinctive images, while concurrently navigating towards the target image. The performance of our active localization and navigation algorithm is demonstrated experimentally on a robot moving within a large outdoor environment.