Sparse mixed ICP registration method

Sparse registration can effectively solve the interference by outliers and noise in the registration procedure and thus has become a useful method. However, experimental research on noncharacteristic point cloud registration with sparse ICP (point to point) and sparse TDM (point to plane) reveals that sparse ICP easily converges to wrong local minima, and sparse TDM easily slides in the tangent plane. To overcome these shortcomings, this study proposes a sparse mixed ICP algorithm that combines the solving procedure of sparse ICP and sparse TDM by using the sigmoid weight function. Sparse mixed ICP can avoid convergence to the wrong local minima; moreover, it can restrain the sliding in the tangent plane and retain sparse registration capabilities to handle outliers and noise. Meanwhile, the success rate of the registration is increased by subdividing the bounding box to decrease the outliers effectively. Experiment results show that the proposed algorithm can handle the registration of noncharacteristic point clouds effectively and efficiently and has strong robustness of point cloud registration with different levels of outliers and noise.