Generic Merging of Structure from Motion Maps with a Low Memory Footprint
This addresses the need for scalable map merging in crowdsourced image data for applications like robotics and mapping, but it is incremental as it builds on existing optimization methods.
The paper tackles the problem of merging multiple Structure from Motion maps efficiently by introducing a low-memory representation that enables robust, order-invariant merging, loop closing, and change detection, achieving verification with simulated and real data from mobile phones and drones.
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.