<title>Landmark-based robot navigation enhanced with color interest operators</title>

摘要

The most important and fascinating ability of natural vision systems is that they spend most of their time on interesting portions of their input, that is, on those aspects of an image which inform the task at hand. This helps a great deal in estimating the location of the system even under dynamic environmental conditions to which systems are subjected to in everyday life. We propose a model that incorporates such ability in robots. Landmark-based approach to robot navigation requires what we define as 'interest operators' to estimate the utility of a particular image region as an effective representative. We have chosen color as the distinguishing characteristic for landmarks. We present a color interest operator consisting of a weighted combination of heuristic scores which thereby selects those image regions or landmarks likely to be found again, even under a different viewing and/or different illumination conditions. The salient regions yield a robust representation for the recognition of a scene. The ability to reproduce regions selected by the operator can be of great help in eliminating environmental uncertainties. We also incorporate a novel color object algorithm, which surpasses all currently available algorithms in speed, robustness and performance to further quicken the response of the navigating robot.