An Efficient Large-Scale 3D Map Stitching Algorithm Using Automatic Overlapping Area Identification

An Efficient Large-Scale 3D Map Stitching Algorithm Using Automatic Overlapping Area Identification

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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 Used Skates 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% DSLR Camera 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°.