An Adaptive Point Cloud Registration Algorithm Based on Cross Optimization of Local Feature Point Normal and Global Surface

The decline of point cloud registration efficiency caused by bad initial position and disordered registration direction has not been effectively solved. Herein, we propose a robust registration algorithm to tackle these drawbacks. First, a novel automatic point cloud alignment strategy considering the normal vector of feature points is demonstrated. This strategy ensures fast convergence in the case of bad initial position. Second, we introduce a cross iterative optimization strategy, which combines the alignment algorithm with an improved ICP (Point-Surface ICP) version based on surface constraints to complete faster and more orderly registration. In order to reduce the computational complexity, we present a linearization for the Point-Surface ICP based on Rodrigues rotation parameterization with the small incremental rotation assumption. In the elimination of outliers, we use the normal distribution of multiple errors to automatically select the threshold interval. Eventually, a large number of experiments are conducted on some public data-sets for performance evaluation of the as-proposed algorithm. Compared with other optimal methods, our method achieves a 17.1% and 58.98% increase in registration accuracy in Dragon dataset and Armadillo dataset, respectively, indicating the higher superiority of our algorithm.