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http://dx.doi.org/10.5574/KSOE.2016.30.5.426

VFH+ based Obstacle Avoidance using Monocular Vision of Unmanned Surface Vehicle  

Kim, Taejin (Korea Research Institute of Ships & Oceans engineering(KRISO))
Choi, Jinwoo (Korea Research Institute of Ships & Oceans engineering(KRISO))
Lee, Yeongjun (Korea Research Institute of Ships & Oceans engineering(KRISO))
Choi, Hyun-Taek (Korea Research Institute of Ships & Oceans engineering(KRISO))
Publication Information
Journal of Ocean Engineering and Technology / v.30, no.5, 2016 , pp. 426-430 More about this Journal
Abstract
Recently, many unmanned surface vehicles (USVs) have been developed and researched for various fields such as the military, environment, and robotics. In order to perform purpose specific tasks, common autonomous navigation technologies are needed. Obstacle avoidance is important for safe autonomous navigation. This paper describes a vector field histogram+ (VFH+) based obstacle avoidance method that uses the monocular vision of an unmanned surface vehicle. After creating a polar histogram using VFH+, an open space without the histogram is selected in the moving direction. Instead of distance sensor data, monocular vision data are used for make the polar histogram, which includes obstacle information. An object on the water is recognized as an obstacle because this method is for USV. The results of a simulation with sea images showed that we can verify a change in the moving direction according to the position of objects.
Keywords
Unmanned surface vehicle; Autonomous navigation; Obstacle avoidance; Vector field histogram+; Monocular vision;
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1 Bay, H., Ess, A., Tuytelaars, T., Van Gool, L., 2008. Speeded-up Robust Features (SURF). Computer Vision and Image Understandin, 110(3), 346-359.   DOI
2 Borenstein, J., Koren, Y., 1991. The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots. IEEE Journal of Robotics and Automation, 7(3), 278-288.   DOI
3 Caccia, M., Bibuli, M., Bono, R., Bruzzone, Ga., Bruzzone, Gi., Spirandelli, E., 2007. Unmanned Surface Vehicle for Coastal and Protected Waters Applications: the Charlie Project. Marine Technology Society Journal, 41(2), 62–71.   DOI
4 Caccia, M., Bibuli, M., Bono, R., Bruzzone, G., 2008. Basic Navigationm Guidance and Control of an Unmanned Surface Vehicle. Autonomous Robots, 25(4), 349–365.   DOI
5 Ester, M., Kriegel, H. P., Sander, J., Xu, X., 1996. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the second International Conference on Kowledge Discoery and Data Mining (KDD), 96(34), 226-231.
6 Kim, D., Shin, J.U., Kim, H., Lee, D., Lee S.M., Myung, H., 2012. Development of Jellyfish Removal Robot System JEROS. Proceedings of Ubiquitous Robots and Ambient Intelligence (URAI), 2012 9th International Conference on IEEE, Daejeon Korea, 599–600.
7 Leutenegger, S., Chli, M., Siegwart, R.Y., 2011. BRISK: Binary Robust Invariant Scalable Keypoints. Proceedings of International Conference on Computer Vision, Barcelona Spain, 2548-2555.
8 Lowe, D.G., 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91-110.   DOI
9 Ulrich, I., Borenstein, J., 1998. VFH+: Reliable Obstacle Avoidance for Fast Mobile Robots. Proceedings of International Conference on Robotics and Automation IEEE, Leuven Belgium, 1572-1577.