Robust Mapping for the Autonomous Mobile Robot Considering Potential Occupied Spaces of Objects

Simultaneous Localization and Mapping (SLAM) is an important function for autonomous mobile robots. 2D or 3D maps under static or dynamic environments have been greatly developed and widely used for robot navigation and path planning. Most of the generated maps can accurately reflect the objects in the environment, but the properties of the objects have not been considered. The robot can avoid colliding with obstacles when using these kind of maps. However, the robot needs to move in a socially acceptable way like human beings. For example, human beings usually avoid moving under desks even if there are paths that can go through. Meanwhile, human beings has the ability to analyze the motion of the objects like a door and move in a considerate way without staying behind it and standing in the way. The spaces under a desk, behind a door, in front of a refrigerator etc. are not occupied by real objects but actually occupied by the objects because of their properties. These kinds of spaces are defined as potential occupied spaces in this paper and considered when generating the map. The objects in the environment are detected and reflected to the may in the same way of conventional methods. Besides, the objects are also recognized and their properties are analyzed to generated virtual areas in the map. In this way, human beings will naturally avoid entering these potentially occupied spaces and the robots can move considerately like human beings. The basic map is generated by immobile area grid map based SLAM. The objects are recognized by Single Shot multi-box Detector (SSD) and other methods, and their potential occupied spaces are generated and reflected to the map base on potential filed method. The effectiveness of the proposed method is proven by mapping under the indoor environment.