Concurrent Mapping and Localization using Range Sonar in Small AUV, SNUUVI

  • Hwang Arom (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Seong Woojae (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Choi Hang Soon (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Lee Kyu Yuel (Department of Naval Architecture and Ocean Engineering, Seoul National University)
  • Published : 2005.12.01

Abstract

Increased usage of AUVs has led to the development of alternative navigational methods that use the acoustic beacons and dead reckoning. This paper describes a concurrent mapping and localization (CML) scheme that uses range sonars mounted on SNUUV­I, which is a small test AUV developed by Seoul National University. The CML is one of such alternative navigation methods for measuring the environment that the vehicle is passing through. In addition, it is intended to provide relative position of AUV by processing the data from sonar measurements. A technique for CML algorithm which uses several ranging sonars is presented. This technique utilizes an extended Kalman filter to estimate the location of the AUV. In order for the algorithm to work efficiently, the nearest neighbor standard filter is introduced as the algorithm of data association in the CML for associating the stored targets the sonar returns at each time step. The proposed CML algorithm is tested by simulations under various conditions. Experiments in a towing tank for one dimensional navigation are conducted and the results are presented. The results of the simulation and experiment show that the proposed CML algorithm is capable of estimating the position of the vehicle and the object and demonstrates that the algorithm will perform well in the real environment.

Keywords

References

  1. Bar-Shalom Y. and T.E. Forman. 1988. Tracking and Data Association, Academic Press
  2. Bar-Shalom Y. and X. Li. 1993. Estimation and Tracking: Principles, Technique, and Software. Artech House Inc
  3. Carpenter R.N. 1998. Concurrent mapping and localization with FLS, Proceedings of AUV 98, 133-148
  4. Harris J.D. and R.N. Carpenter. 2001. Results from the simulation of a decoupled concurrent mapping and localization technique, 4, 2366-2376
  5. Hwang A and W. Seong. 2004. Concurrent mapping and localization for controlling the heading angle of SNNUV I in towing tank. Proc. the Annual Autumn Meeting, SNAK 431-437
  6. Lee C.M., P.M. Lee, S.M. Kim, S.W. Hong, J.W. Seo and W.J Seong. 2003. Rotating arm test for assessment of underwater hybrid navigation system for a semi-autonomous underwater vehicle. J. of KOCRE, 17, 4, 73-80
  7. Newman P.M. 1999. On the Structure and Solution for the Simultaneous Localisation and Map building Problem. PhD Thesis, Australian Centre for Field Robotics, The University of Sydney
  8. Smith C.M, J.J. Leonard, A.A. Bennett and C. Shaw. 1997. Feature-based concurrent mapping and localization for autonomous underwater vehicles. Oceans 1997, 2, 896-901 https://doi.org/10.1109/OCEANS.1997.624111
  9. Smith R., M. Self and P. Cheeseman. 1990. Estimating Uncertain Spatial Relationships in Robotics, in I. Cox and G Wilfong, editors, Autonomous Robot Vehicles, Springer-Verlag