Real-Time Updating of 2D Map for Autonomous Robot Locomotion Based on Distinction Between Static and Semi-Static Objects

Abstract

Abstract Autonomous mobile robots are increasingly being used in complex 2D environments such as factories, warehouses, and offices. For such environments, we propose a real-time technique for updating an environmental map for a robot’s self-localization using a bearing-range sensor in situations where a basis map can be preliminarily prepared. These environments include many semi-static objects such as cardboard boxes, and the locations of these objects change frequently. Therefore, the self-localization needs to reflect the changes in both the existence and position of semi-static objects in the map in real-time. However, if the robot uses a traditional technique that updates all objects and if it keeps updating the map for a long period, static objects such as walls will move slightly on the map due to errors of both measurement and self-localization, and the map will be distorted. Therefore, our technique distinguishes between static and semi-static objects on the map, and it defines the changeability of the occupancy probability of every spatial grid in order to update the map without changing the occupancy probabilities of grids around static objects. By doing so, we prevented the map from being distorted. In addition, by estimating the grids’ statuses during two observations of the same grids and by changing the probabilities of the objects’ fixedness based on the statuses, our technique can robustly distinguish the objects on the map even if the timing of observing grids is irregular.