Abstract
It is popular to use three-dimensional sensing devices such as LiDAR and RADAR for autonomous navigation of ground vehicles in modern approaches. However, there are significant problems: the price of 3D sensing devices, the cost for 3D map building, the robustness against errors accumulated in long-term moving. Visual navigation based on a topological map using only cheap cameras as external sensors has potential to solve these problems; road-following and intersection recognition can enable robust navigation. This paper proposes a novel scheme for intersection recognition using results of semantic segmentation, which has a high affinity for vision-based road-following strongly depending on semantic segmentation. The proposed scheme mainly composed of mode filtering for a segmented image and similarity computation like the Hamming distance showed that good accuracy for the Tsukuba-Challenge 2018 dataset constructed by the authors: perfect results were obtained for more than half intersections included in the dataset. In addition, a running experiment using the proposed scheme with vision-based road-following showed that the proposed scheme could classify intersections appropriately in actual environments.
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This research was partially supported by Research Project Grant(B) by Institute of Science and Technology, Meiji University.
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Ishida, H., Matsutani, K., Adachi, M., Kobayashi, S., Miyamoto, R. (2019). Intersection Recognition Using Results of Semantic Segmentation for Visual Navigation. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_15
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