A Comparison of Two Approaches for Vision and Self-Localization on a Mobile Robot

This paper considers two approaches to the problem of vision and self-localization on a mobile robot. In the first approach, the perceptual processing is primarily bottom-up, with visual object recognition entirely preceding localization. In the second, significant top-down information is incorporated, with vision and localization being intertwined. That is, the processing of vision is highly dependent on the robot´s estimate of its location. The two approaches are implemented and tested on a Sony Aibo ERS-7 robot, localizing as it walks through a color-coded test-bed domain. This paper´s contributions are an exposition of two different approaches to vision and localization on a mobile robot, an empirical comparison of the two methods, and a discussion of the relative advantages of each method.