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In this paper, a new approach to image matching based on both entropy and mutual information measures that can correctly perform association under very low overlap conditions is presented. The method is feature-based, working on a list of interest points detected on the acquired images. Unlike the classical SSD/RanSaC method, our algorithm is robust to the existence of many similar regions in the...
In robotics, vertical lines have been always very useful for autonomous robot localization and navigation in structured environments. This paper presents a robust method for matching vertical lines in omnidirectional images. Matching robustness is achieved by creating a descriptor which is very distinctive and is invariant to rotation and slight changes of illumination. We characterize the performance...
Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization tasks. In this paper, we address the issues of appearance-based topological and metric localization by introducing a novel group matching approach to select less but more robust features to match the current robot view...
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