Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC

The ability to navigate autonomously is now a key technology in self driving cars, unmanned aerial vehicles (UAV), robot guidance, augmented reality, 3D digitization, sensory network localization and more. This ubiquitous appliance is due to the fact that vision sensors can provide cues to directly solve 6DoF pose estimation problem and does not necessitate external tracking input, such as imprecise GPS, to ego-localize. Many of the problems in these domains can now be addressed by tailor-made pipelines such as SLAM (Simultaneous Localization and Mapping), SfM (Structure From Motion) or multi robot localization (MRL) [KPZK17, CC18]. Nowadays, thanks to the resulting reliable estimates of rotations and translations, many of these pipelines rely on some form of an optimization, such as bundle adjustment (BA) [TMHF99] or 3D global registration [BI17, HH03], that can globally consider the acquired measurements.