This paper presents an autonomous navigation system. Our system is based on an accurate 3D map, which includes “geometric information” (e.g., curb, wall, street tree) and “semantic information” (e.g., sidewalk, roadway, crosswalk) extracted by environmental recognition. By using the semantic map, we can obtain the suitable area to keep away from undesired places. Furthermore, by comparing the map with real-time 3D geometric information from LIDAR, we obtain the robot position. To show the effectiveness of our system, we conduct a 3D semantic map construction experiment and driving test. The experiment results show that the proposed system enables accurate and highly reproducible localization and stable autonomous mobility.
This paper presents a novel autonomous navigation system. Our proposed system is based on a simple map (an Edge-Node Graph, which is created from an electronic map). This system consists of “Localization,” which estimates which edge is on the Edge-Node Graph, “Environmental Recognition,” which recognizes the environment around the robot, and “Path Planning,” which avoids objects. Since the robot travels using the Edge-Node Graph, there is no need to prepare an environmental map in advance. In addition, the system is quite robust, since it relies less on prior information. To show the effectiveness of our system, we conducted experiments on each elemental technology as well as some traveling tests.
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