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ABSTRACT: The paper describes a localization system for autonomous underwater vehicles (AUV). It uses a DVL (Doppler velocity log) sensor and AHRS (attitude and heading reference system) sensor to measure AUV's depth, attitude and velocities relative to the bottom. A mechanically scanning imaging sonar (MSIS) is employed to obtain acoustic images of objects in underwater environment. In order to estimate optimally AUV pose without a priori map of the environment, simultaneous localization and map building (SLAM), a prevailing method in the past decade, is presented based on point features extraction and EKF-based estimator. Use Fluvia Nautic marina data set we compare the proposed method with traditional dead-reckoning, results show that our solution can reduce estimation error significantly.
Mechatronics and Automation, 2009. ICMA 2009. International Conference on; 09/2009
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ABSTRACT: The data logging and management system (DLAM) of the autonomous underwater vehicle (AUV) running on the on-board PC/104 module uses the synchronization and communication mechanism by using multi-threading to implement the function of the collection of data from different devices through multi-serial-port card for sonar, compass, gyro and the USB port for accelerometer, as well as the function of enveloping and transmitting the data to the on-board Industrial PC (IPC) via Ethernet and the function of managing and recording the data locally. Circular buffer management class designed by ourselves and the sequence table are used to manage the data in the memory. By using the circular buffer, we can be free from those trivia like allocating and releasing the resources of memory all the time. The software reliability is analysed and evaluated as well. White-box testing method is employed to test the system, and Musa execution time model is used to estimate the reliability. The result of the estimation shows that the system achieves the expected requirement.
Information Engineering, International Conference on. 07/2009; 1:75-78.
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FSKD 2009, Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, 14-16 August 2009, 6 Volumes; 01/2009
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ABSTRACT: Data association is one of the most difficult problems in Simultaneous Localization and Mapping (SLAM). As for Autonomous Underwater Vehicle (AUV), reliable data association is particularly important because of complex and mutable underwater environment. In this paper two prevailing data association algorithms—Individual Compatibility Nearest Neighbor (ICNN) and Joint Compatibility Branch and Bound (JCBB) are compared by simulation experiments and then some improvements on the computational complexity of JCBB are presented in order to seek a robust data association method for real-time application of our AUV. The SLAM algorithm used in the experiments is based on Extended Kalman Filter (EKF).
Measuring Technology and Mechatronics Automation, International Conference on. 1:886-889.
