Realizing, reversing, recovering: Incremental robust loop closing over time using the iRRR algorithm

The ability to reconsider information over time allows to detect failures and is crucial for long term robust autonomous robot applications. This applies to loop closure decisions in localization and mapping systems. This paper describes a method to analyze all available information up to date in order to robustly remove past incorrect loop closures from the optimization process. The main novelties of our algorithm are: 1. incrementally reconsidering loop closures and 2. handling multi-session, spatially related or unrelated experiments. We validate our proposal in real multi-session experiments showing better results than those obtained by state of the art methods.