A compact planar-patch descriptor based on color

The representation of the world upon planar patches has proven to be simple, robust and useful for a variety of robotic tasks, including SLAM, autonomous navigation, or scene recognition. In this work we investigate how to incorporate color information into such representation to improve the matching of planar patches while maintaining the model compactness, which is essential for real-time applications. We propose a descriptor based on the dominant color of the patch, which is defined as the center of the biggest cluster in the patch histogram. In the paper, different color spaces and methods for extracting the dominant color are analyzed. We compare this descriptor with a recent proposal (saturated hue based histogram) and provide some conclusions on the trade-off between their descriptiveness and compactness. Finally, we present experimental results showing how our color descriptor can be exploited to increase the efficiency of both: plane-based place recognition and planar patch categorization.