In this study, we propose a navigation system that guides a robot at a location visited for the first time, without developing a map in advance. First, it estimates the position of a path that exists on the local map by matching the metric route information and the local map generated by simultaneous localization and mapping (SLAM); this is achieved by using a particle filter. Then, the robot travels to the destination along the estimated route. In this system, the geometric accuracy of the route information specified in advance and the accuracy of the map generated by SLAM are essential. Furthermore, it is necessary to recognize the traversable area. The experiment performed verifies the matching of the route information and local map. In the autonomous running experiment, we conduct a trial run on a course set up at the University of Tsukuba.
In order for a robot to provide service in a real world environment, it has to navigate safely and recognize the surroundings. We have participated in Tsukuba Challenge to develop a robot with robust navigation and accurate object recognition capabilities. To achieve navigation, we have introduced the ROS packages, and the robot was able to navigate without major collisions throughout the challenge. For object recognition, we used both a laser scanner and camera to recognize a person in specific clothing, in real time and with high accuracy. In this paper, we evaluate the accuracy of recognition and discuss how it can be improved.
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