Efficient point cloud registration method integrating improved SAC-IA and weighted ICP

Abstract:

Traditional Iterative Closest Point (ICP) algorithms are susceptible to local optima and sensitive to initial pose when applied to large-scale structured scenes, such as underground tunnels, due to their repetitive geometric features. To address these challenges, this paper proposes an efficient two-stage point cloud registration framework. In the coarse registration stage, an improved Sample Consensus Initial Alignment (SAC-IA) algorithm with an enhanced keypoint sampling strategy is introduced to rapidly obtain a globally optimal initial pose from the downsampled point cloud. Subsequently, the fine registration stage employs a novel multi-dimensional weighted ICP algorithm. This algorithm integrates Euclidean distance, normal vector angles, and curvature information into a comprehensive geometric objective function, effectively reducing the mismatch rate in the planar regions of tunnels.The proposed components were individually validated on the Stanford public dataset. Results show that the improved coarse registration algorithm increases computational efficiency by 44.1% over the conventional SAC-IA, while the geometry-based weighted ICP algorithm improves registration accuracy by 23.81% and reduces computation time by 25.9% compared to the traditional ICP. Comprehensive experiments on real-world tunnel point clouds demonstrate that the proposed framework significantly outperforms traditional methods, improving both overall accuracy and efficiency by over 20%. Furthermore, compared to mainstream deep learning methods, this framework obviates the need for high-performance GPUs. It achieves a several-fold increase in processing efficiency on a standard CPU and delivers more robust registration results in realistic tunnel scenarios. Ultimately, this framework provides a highly efficient solution for automatic point cloud registration in large-scale structured environments, fully satisfying industrial demands for real-time processing and lightweight implementation.