Abstract
Bundle adjustment has been considered as one of the most important components in many visual tasks such as 3D reconstruction, photo grammetry, visual SLAM, etc. Unfortunately, both time and space complexity of this adjustment prevent it from being directly applied to large scale datasets. This paper presents a sub mapping method, which partitions a large scale dataset into disjointed subsets and adjusts them one by one or in parallel. Pair-wise sub maps are then stitched together by applying a similarity transformation. Both simulations and real applications show that our method scales well. Also some basic questions of this sub mapping method including map size, map fusion and global consistency are discussed.
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