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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Haneda robotics lab. https://www.tokyo-airport-bldg.co.jp/hanedaroboticslab/
Velodyne lidar. https://velodynelidar.com/
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Chen, T.W., Chen, Y.L., Chien, S.Y.: Fast image segmentation based on k-means clustering with histograms in HSV color space. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, pp. 322–325. IEEE (2008)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: The IEEE Conference on Computer Vision and Pattern Recognition, June 2016
He, X., Zemel, R., Carreira-Perpinan, M.: Multiscale conditional random fields for image labeling, vol. 2, pp. II-695–II-702, 27 June–2 July 2004
Kato, Z., Pong, T.C.: A markov random field image segmentation model for color textured images. Image Vis. Comput. 24(10), 1103–1114 (2006)
Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Trans. Geosci. Remote Sens. 50(3), 809–823 (2011)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Miyamoto, R., Adachi, M., Nakamura, Y., Nakajima, T., Ishida, H., Kobayashi, S.: Accuracy improvement of semantic segmentation using appropriate datasets for robot navigation. In: Proceedings of the International Conference on Control, Decision and Information Technologies, pp. 1610–1615 (2019)
Miyamoto, R., Nakamura, Y., Adachi, M., Nakajima, T., Ishida, H., Kojima, K., Aoki, R., Oki, T., Kobayashi, S.: Vision-based road-following using results of semantic segmentation for autonomous navigation. In: Proceedings of the International Conference of Consumer Electronics in Berlin, pp. 194–199 (2019)
Patras, I., Hendriks, E.A., Lagendijk, R.L.: Video segmentation by map labeling of watershed segments. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 326–332 (2001)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Tao, W., Jin, H., Zhang, Y.: Color image segmentation based on mean shift and normalized cuts. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(5), 1382–1389 (2007)
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 418–434. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_25
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
Zhao, H., et al.: PSANet: point-wise spatial attention network for scene parsing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 270–286. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_17
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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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