A Hybrid Bayesian-Frequentist Approach to SLAM

A hybrid Bayesian/ frequentist approach is presented for the Simultaneous Localization and Mapping Problem (SLAM). A frequentist approach is proposed for mapping a dense environment when the robotic pose is known and then extended to the case when the pose is uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The frequentist part of the hybrid methodology is shown to have complexity linear (constant time complexity under the assumption of bounded noise) in the map components, is robust to the data association problem and is provably consistent. The complexity of the Bayesian part is kept under control owing to the sparseness of the features, which also improves the robustness of the technique to the issue of data association. The hybrid method is tested on standard datasets on the RADISH repository.