The detection and description of features is one basic technique for many visual robot navigation systems in both indoor and outdoor environments. Matched features from two or more images are used to solve navigation problems, e.g., by establishing spatial relationships between different poses in which the robot captured the images. Feature detection and description is particularly challenging in outdoor environments, and widely used grayscale methods lead to high numbers of outliers. In this paper, we analyze the use of color information for keypoint detection and description. We consider grayscale and color-based detectors and descriptors, as well as combinations of them, and evaluate their matching performance. We demonstrate that the use of color information for feature detection and description markedly increases the matching performance.
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