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

TL;DR

This paper introduces a robust, low-memory map merging algorithm for Structure from Motion data that is invariant to merging order, coordinate systems, and supports hierarchical updates, loop closing, and change detection, suitable for large-scale image datasets.

Contribution

The paper presents a novel low-memory, order-invariant map merging method that enables hierarchical updates and change detection in SfM maps, applicable to large image datasets.

Findings

Effective in merging multiple maps with low memory usage

Invariant to merging order and coordinate system

Validated on simulated and real-world data from mobile and drone sources

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.