Fine registration of 3D point clouds with iterative closest point using an RGB-D camera

We address the problem of accurate and efficient alignment of 3D point clouds captured by an RGB-D (Kinect-style) camera from different viewpoints. Our approach introduces a new cost function for the iterative closest point (ICP) algorithm that balances the significance of structural and photometric features with dynamically adjusted weights to improve the error minimization process. We also enhance the algorithm with a novel outlier rejection method, which relies on adaptive thresholding at each ICP iteration, using both the structural information of the object and the spatial distances of sparse SIFT feature pairs. The effectiveness of our proposed approach is demonstrated in challenging scenarios, involving objects lacking structural features, and significant camera view and lighting changes. We obtained superior registration accuracy than existing related methods while requiring low computational processing.