Current state of the art scan registration algorithms which use only position information often fall victim to correspondence ambiguity and degeneracy in the optimization solutions. Other methods which use additional channels, such as color or intensity, often use only a small fraction of the available information and ignore the underlying structural information of the added channels. The proposed method incorporates the additional channels directly into the scan registration formulation to provide information within the plane of the surface. This is achieved by calculating the uncertainty both along and perpendicular to the local surface at each point and calculating nearest neighbor correspondences in the higher dimensional space. The proposed method reduces instances of degenerate transformation estimates and improves both registration accuracy and convergence rate. The method is tested on the Ford Vision and Lidar dataset using both color and intensity channels, as well as with Microsoft Kinect data from the Freiburg RGBD Office dataset and data obtained from the University of Waterloo campus.
['James Servos', 'Steven Lake Waslander']
Multi-Channel Generalized-ICP: A robust framework for multi-channel scan registration