Linear-complexity stochastic variational Bayes inference for SLAM

The simultaneous localization and mapping (SLAM) problem is concerned with using sensor data to build an environmental map, while also localizing an autonomous agent within this map. Two approaches are currently prevalent (Bayesian filtering and graph-based optimization), however these both involve approximations and have the potential to be improved. In this paper, we propose novel high-performance SLAM algorithms derived from variational Bayes inference. By using mean-field type approximations, the resulting computational complexity is linear. We also add an empirical Bayes assumption to improve the flexibility of the inference. Experiments are conducted on both synthetic data and real RGB-D images. The proposed approach achieves 42% average error reduction in all scenarios on synthetic data, and 26% average error reduction on real images (with respect to two other baseline algorithms).