HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration

To address the persistent computational bottlenecks in point cloud registration, this paper proposes a hierarchical grouping strategy named HiGoReg. This method incrementally updates the pose of the source point cloud via a hierarchical mechanism, while adopting a grouping strategy to efficiently conduct recursive parameter estimation. Instead of operating on high-dimensional matrices, HiGoReg leverages previous group estimates and current observations to achieve precise alignment with reduced computational overhead. The method’s effectiveness was validated using both simulated and real-world datasets. The results demonstrate that HiGoReg attains comparable accuracy to traditional batch solutions while significantly improving efficiency, achieving up to 99.79% speedup. Furthermore, extensive experiments confirmed that optimal performance is achieved when each group contains approximately 100 observations. In contrast, excessive grouping could undermine computational efficiency.