Consistent SLAM using Local Optimization with Virtual Prior Topologies

In the present work we address the problem of achieving a consistent estimator for SLAM. We propose a novel method capable of computing approximately consistent global uncertainties without scaling in complexity with the total size of the explored area. The method allows arbitrary selection of local areas for optimization, introducing a methodology for building a virtual prior in bounded time. The constructed prior topologies serve as a way of contextualizing the local optimizations, resulting in consistent uncertainty estimations. This overcomes several shortcomings of previous approaches that rely on conditioning (fixing variables) and/or sliding window marginalization. Evaluations are presented in different simulated scenarios comparing results against a global batch optimization and other canonical approaches.