Slam with expectation maximization for moveable object tracking

Abstract

The goal of simultaneous localization and mapping (SLAM) is to compute the posterior distribution over landmark poses. Typically, this is made possible through the static world assumption - the landmarks remain in the same location throughout the mapping procedure. Some prior work has addressed this assumption by splitting maps into static and dynamic sets, or by recognizing moving landmarks and tracking them. In contrast to previous work, we apply an Expectation Maximization technique to a graph based SLAM approach and allow landmarks to be dynamic. The batch nature of this operation enables us to detect moveable landmarks and factor them out of the map. We demonstrate the performance of this algorithm with a series of experiments with moveable landmarks in a structured environment.