Proposal of a spectral random dots marker using local feature for posture estimation

We propose a novel marker for robot's grasping task which has the following three aspects: (i) it is easy-to-find in a cluttered background, (ii) it is calculable for its posture (iii) its size is compact. The proposed marker is composed of a random dots pattern, and uses keypoint detection and a scale estimation by Spectral SIFT for dots detection and data decoding. The data is encoded by the scale size of dots, and the same dots in the marker work for both marker detection and data decoding. As a result, the proposed marker size can be compact. We confirmed the effectiveness of the proposed marker through experiments.