Simultaneous localization and mapping in dense environments

A hybrid Bayesian/ frequentist approach is presented for the simultaneous localization and mapping problem (SLAM). A frequentist approach is proposed for mapping with time varying robotic poses and is generalized to the case when the robotic pose is both time varying and 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 hybrid methodology is shown to have complexity linear in the map components, is robust to the data association problem and is provably consistent.