Abstract
In order to move around automatically, mobile robots usually need to recognize their working environment first. Simultaneous localization and mapping (SLAM) has become an important research field recently, by which the robot can generate a map while moving around. Both two-dimensional (2D) mapping and three-dimensional (3D) mapping methods have been developed greatly with high accuracy. However, 2D maps cannot reflect the space information of the environment and 3D mapping needs long processing time. Moreover, conventional SLAM methods based on grid maps take a long time to delete the moving objects from the map and are hard to delete the potential moving objects. In this paper, a 2D mapping method integrating with 3D information based on immobile area occupied grid maps is proposed. Objects in 3D space are recognized and their space information (e.g., shapes) and properties (moving objects or potential moving objects like people standing still) are projected to the 2D plane for updating the 2D map. By using the immobile area occupied grid map method, recognized still objects are reflected to the map quickly by updating the immobile area occupancy probability with a high coefficient. Meanwhile, recognized moving objects and potential moving objects are not used for updating the map. The unknown objects are reflected to the 2D map with a lower immobile area occupancy probability so that they can be deleted quickly once they are recognized as moving objects or start to move. The effectiveness of our method is proven by experiments of mapping under dynamic indoor environment using a mobile robot.
Highlights
Autonomous mobile robots have been greatly developed, so that the research field of simultaneous localization and mapping (SLAM) has been noticed, since the almost all kinds of mobile robots need this method for environment understanding, navigation, and path planning [1,2,3]
Generated 2D maps have been widely applied for navigation and path planning for mobile robots, but 2D maps cannot reflect space information, so the robot might collide with the objects
Robots tend to move safely and efficiently if the map is more accurate and able to deal with dynamic environment. 2D maps generated by open sources like gmapping [21] are widely used for many kinds of service robots nowadays, because 3D mapping methods usually need more processing time for saving and updating the space information [6,7,8]
Summary
Autonomous mobile robots have been greatly developed, so that the research field of simultaneous localization and mapping (SLAM) has been noticed, since the almost all kinds of mobile robots need this method for environment understanding, navigation, and path planning [1,2,3]. Two dimensional (2D) mapping methods based on laser sensors have been widely used, since these kinds of sensors can detect the distances of the obstacles around the robot with a high accuracy stably [4,5]. The moving objects can be deleted from the map gradually with time going on [4]. 2D mapping methods based on laser sensors still have some problems. Three-dimensional (3D) space information cannot be detected by laser sensors. The obstacles can only be detected when they are in the same height with the sensors. Objects with special shapes cannot be reflected to the map correctly, which will cause collisions when the robot moves around according to the map information
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