Robust Mapping for Mobile Robot Based on Immobile Area Grid Map Considering Potential Moving Objects

Mobile robots need Simultaneous Localization and Mapping (SLAM) for autonomous movement in human living environments. The occupancy grid map used in SLAM is a conventional method which makes a map by an occupancy probability in each grid. This method renews a map based on whether an object is observed or not. In order to remove moving objects from a map, an additional method is required. However, conventional methods deal only with actually moving objects, and potential moving objects (e.g., standing humans) are mapped as static objects. Furthermore, only binary states, used or not used, are given to each object in map updating. This paper proposes the immobility area grid map to represent a map by an immobility probability in each grid. The proposed method renews a map based on the identification of observed objects by a robot's sensors, in addition to whether an object is observed or not. We introduce the map update parameter, which is set adaptively from the certainty of identification result of the object. Observed objects can take continuous states, truly staticunknowntruly moving, according to the parameter value. Potential moving objects are not mapped if the parameter takes values corresponding to moving objects. The experimental results show robust mapping in dynamic environments including potential moving objects.