An Efficient Large-Scale 3D Map Stitching Algorithm Using Automatic Overlapping Area Identification - Hsien-I Lin; Muhammad Ahsan Fatwaddin Shodiq; An-Kai Jeng; Chun-Wei Chang (IEEE Access, 2025)
Quality of 3D point cloud maps is essential for navigation and localization in Autonomous Mobile Robots, yet creating these maps for large-scale areas presents challenges, stemming from the processing of numerous points. In such situations, constructing a 3D map can be accomplished by dividing it into smaller regions and then merging them to generate a complete map by performing a 3D map stitching algorithm. Currently, these overlapping areas are manually selected, which leads to potential errors. In response, a novel method to automatically identify overlapping areas is proposed to perform map stitching based on the overlapping areas only instead of the entire maps. Utilizing the proposed method results in a significant reduction in time consumption. The proposed automatic method incorporates the DBSCAN algorithm for clustering, template matching for identifying corresponding clusters, and a binary-search algorithm for parameter optimization. The proposed method was evaluated on several large-scale 3D maps, including the KITTI dataset, and compared against manual selection and the use of entire maps in the map-merge-3D algorithm. The method achieves a significant reduction in the time required for the 3D map stitching process, amounting to a 38.64% decrease compared to using the entire maps. In terms of accuracy, the proposed method reduces translation error to 0.1723m and rotation error to 0.1763°, representing decreases of 5.28% and 16.16%, respectively, while manual selection results in a translation error of 0.4278m and rotation error of 0.7123°, increases of 135.25% and 238.75% respectively, compared to the entire maps 0.1819m and 0.2103°.