Point Cloud Registration via Semantic Grid Segmentation

The patent segments the point cloud data into multiple grids (spatial partitioning), processing each grid independently rather than handling all point clouds globally. This divides the computational task into smaller sub-problems that can be solved more efficiently while maintaining registration accuracy through localized feature matching.

The patent applies different processing strategies to different spatial regions by dividing the point cloud into grids. Each grid can be processed with appropriate feature extraction or direct matching based on local characteristics, making the method less sensitive to noise in specific regions while maintaining overall registration accuracy.

The patent creates a universal registration framework that can handle both feature-rich and feature-poor scenes. By combining grid-based processing with optional feature extraction, the method adapts to different scene types - using features when available for efficiency, and falling back to direct point matching when features are absent, thus improving versatility across diverse applications.

The patent introduces semantic dimension to the traditional spatial rasterization by classifying points into ground and non-ground categories. This additional semantic dimension allows the method to distinguish between relevant and irrelevant features, reducing interference from ground vehicles and improving performance in high-similarity scenes where traditional reflectivity-based methods fail.