Abstract
The current tendency in mobile robot indoor navigation is to move from the representation environment as a geometric grid map to a topological and semantic map closer to the way how humans reason. The topological and semantic map enables a robot to understand the environment. This paper presents a topological and semantic segmentation algorithm that divides a grid map into single rooms or similar meaningful semantic units with a collision-free path to connect them. First, a topological map is build based on the distance transform of the grid map. Then a semantic map is build based on the distance transform of the grid map and a circular kernel. Finally, we filter and prune the topological map by merging the nodes which represent the same room. The segmented performance of the proposed planning framework is verified on multiple maps. The experiment results show that the proposed method can accurately segment rooms and generate topological semantic maps.
This work was supported partially by the National Key Research and Development Program of China (2020YFB1313900), and partially by the Shezhen Science and Technology Program (No. JCYJ20180508152226630).
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Chen, Y., Zhang, J., Lou, Y. (2021). Topological and Semantic Map Generation for Mobile Robot Indoor Navigation. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13013. Springer, Cham. https://doi.org/10.1007/978-3-030-89095-7_33
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DOI: https://doi.org/10.1007/978-3-030-89095-7_33
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