Generic Merging of Structure from Motion Maps with a Low Memory Footprint

• URL: http://arxiv.org/abs/2103.13246v1
• Date: Wed, 24 Mar 2021 15:03:25 GMT
• Title: Generic Merging of Structure from Motion Maps with a Low Memory Footprint
• Authors: Gabrielle Flood, David Gillsj\"o, Patrik Persson, Anders Heyden, Kalle \r{A}str\"om
• Abstract summary: We present new tools that will enable efficient, flexible and robust map merging. Using both simulated and real data - from both a hand held mobile phone and from a drone - we verify the performance of the proposed method.
• Score: 3.7838598767969502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged. This calls for development of ways to handle all these data. In this paper, we present new tools that will enable efficient, flexible and robust map merging. Assuming that separate optimisations have been performed for the individual maps, we show how only relevant data can be stored in a low memory footprint representation. We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system. The result is a robust algorithm that can be applied to several maps simultaneously. The result of a merge can also be represented with the same type of low-memory footprint format, which enables further merging and updating of the map in a hierarchical way. Furthermore, the method can perform loop closing and also detect changes in the scene between the capture of the different image sequences. Using both simulated and real data - from both a hand held mobile phone and from a drone - we verify the performance of the proposed method.

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