Abstract
This paper describes a localization method to localize a mobile vehicle in underwater environment. Particle filter based localization method is implemented, which is based on Bayesian filter to deal with non-linear system. This method comprises prediction step and correction step to estimate the pose of the vehicle. The prediction step is achieved by a motion model of the vehicle with inertial sensor data acquired from Doppler Velocity Log, inertia sensors, and electronic compass. In the correction step, the pose of the vehicle is updated using range and bearing information of externally fixed landmarks in the vehicle work space. The performance of the proposed localization method is verified by experiment in a tank environment using four artificial landmarks. In the experiment, the motion information of the underwater vehicle is used as surge and yaw velocities obtained from DVL and AHRS sensors. The landmark information is acquired from the artificial landmarks using an image sensor. The experimental result shows that the proposed method successfully estimated the pose of the vehicle.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)
Song, K.S., Chu, P.C.: Conceptual Design of Future Undersea Unmanned Vehicle (UUV) System for Mine Disposal. IEEE Systems Journal 6(4) (August 2012)
Moon, Y.S., Ko, N.Y., Sur, J., Lee, Y.O.: Development of Torpedo Type Mine Disposal Robot RUCUR MK-1. In: Proceedings of the Ninth International Symposium on Technology and the Mine Problem, pp. 17–21. Naval Postgraduate School, Monterey (2010)
Alameda, W.: Seadevil: A totally integrated inertial navigation solution. In: Proc. Underwater Intervention Symp., New Orleans, LA (2002)
Christ, R.D., Wernli Sr., R.L.: The ROV Manual: A User Guide for Observation Class Remotely Operated Vehicles, p. 320. Butterworth-Heinemann (August 2007)
Cheng, W.H.: Study mobile underwater positioning system with expendable and multi-functional bathythermographs. Ocean Engineering 32, 499–512 (2005)
Vaganay, J., Bellingham, J.G., Leonard, J.J.: Outlier rejection for autonomous acoustic navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 2174–2181 (April 1996)
Smith, J.O., Abel, J.S.: The Spherical Interpolation Method of Source Localization. IEEE Journal of Oceanic Engineering 12(1), 246–252 (1987)
Alcocer, A., Oliveira, P., Pascoal, A.: Underwater Acoustic Positioning Systems Based on Buoys with GPS. In: Proceedings of the Eighth European Conference on Underwater Acoustics, 8th ECUA, Carvoerio, Portugal (2006)
Wang, J., Gao, Q., Yu, Y., Wang, H., Jin, M.: Toward Robust Indoor Localization Based on Bayesian Filter Using Chirp-Spread-Spectrum Ranging. IEEE Transaction on Industrial Electronics 59(3), 1622–1629 (2012)
Donovan, G.T.: Position Error Correction for an Autonomous Underwater Vehicle Inertial Navigation System (INS) Using a Particle Filter. IEEE Journal of Oceanic Engineering 37(3) (July 2012)
Ko, N.Y., Kim, T.G., Noh, S.W.: Monte Carlo Localization of Underwater Robot Using Internal and External Information. In: 2011 IEEE Asia-Pacific Services Computing Conference Proceedings, pp. 410–15 (2011)
Maki, T., Matsuda, T., Sakamaki, T., Ura, T., Kojima, J.: Navigation Method for Under-water Vehicles Based on Mutual Acoustical Positioning With a Single Seafloor Station. IEEE Journal of Oceanic Engineering 38(1), 167–177 (2013)
Fallon, M.F., Papadopoulos, G., Leonard, J.J., Patrikalakis, N.M.: Cooperative AUV navigation using a single maneuvering surface craft. The International Journal of Robotics Research 29(12), 1461–1474 (2010)
Kim, T.G., Ko, N.Y.: Comparison and Analysis of Methods for Localization of a Mobile Robot. Journal of Korean Institute of Information Technology 11(1), 79–89 (2013)
Lee, D., Kim, G., Kim, D., Myung, H., Choi, H.: Vision-based object detection and tracking for autonomous navigation of underwater robots. Oceanic Engineering 48, 59–68 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kim, T.G., Choi, HT., Ko, N.Y. (2014). Estimation of Vehicle Pose Using Artificial Landmarks for Navigation of an Underwater Vehicle. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_74
Download citation
DOI: https://doi.org/10.1007/978-3-319-05582-4_74
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05581-7
Online ISBN: 978-3-319-05582-4
eBook Packages: EngineeringEngineering (R0)