Contracting curve density algorithm for applications in personal robotics

This paper investigates an extended and optimized implementation of the state-of-the-art local curve fitting algorithm named Contracting Curve Density (CCD) algorithm, originally developed by Hanek et al. In particular, we investigate its application in the field of personal robotics for the tasks such as the mobile manipulation which requires a segmentation of objects in clutter and the tracking of them. The developed system mainly consists of the two functional parts, the CCD algorithm to fit the model curve in still images and the CCD tracker to track the model in the videos. We demonstrate algorithm´s working in various scenes using handheld camera and the cameras from the Personal Robot 2 (PR2). Achieved results show that the CCD algorithm achieves robustness and sub-pixel accuracy even in the presence of clutter, partial occlusion, and changes of illumination.