Vision Based 3D Point Cloud Map Overlapping Region Estimation for Map-Merging

Accurate large-scale maps are essential for reliable navigation of autonomous vehicles and mobile robots in outdoor environments. However, SLAM approaches suffer from drift and high computational costs over extended areas, while pure 3D reconstruction methods incur prohibitive processing times and similarly degrade at scale. This paper introduces a fully automated map-merging framework that fuses multiple vision-based 3D point cloud submaps into a single, large-scale map. Each submap is generated by applying ORB-SLAM3 for RGB-D pose estimation followed by 3D reconstruction via COLMAP. Overlapping regions are estimated automatically by extracting ORB descriptors, matching via DBoW3, and back-projecting correspondences into 3D space; DBSCAN clustering then removes outliers to yield robust overlap subsets. These subsets drive FPFH and TEASER++ registration, whose resulting transformations align all submaps in a common coordinate frame. The proposed method significantly enhances automation and robustness in large-scale map construction.