Sparser Relative Bundle Adjustment (SRBA): Constant-time maintenance and local optimization of arbitrarily large maps

In this paper we defend the superior scalability of the Relative Bundle Adjustment (RBA) framework for tackling with the SLAM problem. Although such a statement was already done with the introduction of the sliding window (SW) solution to RBA [16], we claim that the map extension that can be maintained locally consistent for some fixed computational cost critically depends on the specific pattern in which new keyframes are connected to previous ones. By rethinking from scratch what we call loop closures in relative coordinates we will show the unexploited flexibility of the RBA framework, which allows us a continuum of strategies from pure relative BA to hybrid submapping with local maps. In this work we derive a systematic way of constructing the problem graph which lies close to submapping and which generates graphs that can be solved more efficiently than those built as previously reported in the literature. As a necessary tool we also present an algorithm for incrementally updating all the spanning-trees demanded by any efficient solution to RBA. Under weak assumptions on the map, and implemented on carefully designed data structures, it is demonstrated to run in bounded time, no matter how large the map becomes. We also present experiments with a synthetic dataset of 55K keyframes in a world of 4.3M landmarks. Our C++ implementation has been released as open source.