Maximum Likelihood SLAM in Dynamic Environments

Simultaneous Localization and Mapping in dynamic environments is an open issue in the field of robotics. Traditionally, the related approaches assume that the environment remains static during the robot´s exploration phase. In this work, we overcome this assumption and propose an algorithm that exploits the dynamic nature of the environment during robot exploration so as to improve the localization process. We use a Histogram Grid to store all the past occupancy values of every cell and thus to select the most probable pose of the robot based on the occupancy evolution. Experiments on a simulated robot indicate the effectiveness of the proposed approach.