DATA ASSOCIATION OF RF-VSLAM FOR OCEAN OBSERVATION USING BLIMP

Authors

  • Herdawatie Abdul Kadir Department of Robotic & Mechatronic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia
  • M. R. Arshad Underwater, Control and Robotics Group (UCRG), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia

DOI:

https://doi.org/10.11113/jt.v74.4803

Keywords:

Visual SLAM, beacon, buoy, feature, SIFT, data association

Abstract

This paper describes a selection of features for potential landmarks for ocean observation system using radio frequency visual simultaneous localization and mapping (RF-VSLAM) framework. Due to dynamic changes of the ocean surface caused by the ocean gyres, the features selection is difficult. Therefore, the tendency for vehicles to drift is high. As a solution, we introduced the beacons as an anchor node as an aid to correct the navigation and improve data association. We investigated the data association stage of the RF-VSLAM system which improved the state estimator for the aerial vehicle. The goal is to produce a correct association to the landmarks, since wrong data association will produce inaccurate maps. The points features were extracted from a monocular camera using SIFT as detector and descriptor. The experimental data of the dynamic changes of water surface has been evaluated. The result showed that the data association method was able to produce correct and accurate landmarks selection.  

References

Doney, S. C., Ruckelshaus, M., Duffy, J. E., Barry, J. P., Chan, F., English, C. A. & Talley, L. D. 2012. Climate Change Impacts on Marine Ecosystems. Marine Science. 4.

Pandolfi, J. M., Connolly, S. R., Marshall, D. J., & Cohen, A. L. 2011. Projecting Coral Reef Futures Under Global Warming and Ocean Acidification. Science. 333(6041): 418-422.

Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N., & Bay, R. A. 2014. Mechanisms of reef coral resistance to future climate change. Science. 344(6186):895-898.

Bell, K. L. C., Elliott, V., Martinez, C. and Fuller, S. A. 2011. New Frontiers in Ocean Exploration: The E/V Nautilus and NOAA Ship Okeanos Explorer, Field Season. Oceanography. 25(1): 68.

Allen, B., Stokey, R., Austin, T., Forrester, N., Goldsborough, R., Purcell, M. and von Alt, C. 1997. REMUS: A Small Low Cost AUV: System Description, Field Trials, Performance Results. In Proc. MTS/IEEE OCEANS. 994-1000.

Griffiths, G., Millard, N., McPhail, S., Stevenson, P., Perrett, J., Peabody, M., Webb, A. and Meldrum, D. 1998. Towards Environmental Monitoring with the Autosub Autonomous Underwater Vehicle. In Proc. Int. Symp. Underwater Technology.121-125.

Muljowidodo, K., Budiyono, S. A. N., A. and Prayogo, N. 2009. Design of SHRIMP ROV for Surveillance and Mine Sweeper. 38: 332-337.

Monterey Bay Aquarium Research Institute. MOOS: Monterey Ocean Observing System, [Online]. From http://www.mbari.org/moos/mooring/mooring.htm. [Accessed on 22 August 2014].

Detrick, R. Frye, D. Collins, J. Gobat, J. Grosenbaugh, M. Petitt, R. and Horton E. 2000. DEOS Moored Buoy Observatory Design Study. Woods Hole Oceanographic Institution Technical Report.

Darwin, N., Ahmad, A., Aziz, W. A. W and Akib. M. 2014. The Potential of Low Altitude Aerial Data for Large Scale Mapping. Jurnal Teknologi. 70(5): 109-115.

Klippenstein, J., Hong Zhang. 2009. Performance Evaluation of Visual SLAM Using Several Feature Extractors. IEEE/RSJ International Conference of Intelligent Robots and Systems, IROS. 10-15.

Kadir, H. A., Arshad, M. R. 2013. Features Detection and Matching for Visual Simultaneous Localization and Mapping (VSLAM). IEEE International Conference on Control System, Computing and Engineering. 40-45.

Downloads

Published

2015-06-21

How to Cite

DATA ASSOCIATION OF RF-VSLAM FOR OCEAN OBSERVATION USING BLIMP. (2015). Jurnal Teknologi (Sciences & Engineering), 74(9). https://doi.org/10.11113/jt.v74.4803