Performance Comparison between Several Underwater Navigation Algorithms for Autonomous Underwater Vehicle

Performing and robust navigation systems for Autonomous Underwater Vehicles (AUVs) play a critical role in scientific and industrial missions (e.g. exploration and surveillance of archaeological sites, reconnaissance and patrolling for military operations, Oil & Gas operations). The quality of the navigation algorithm for the estimation of the AUV navigation state affects the performance of the overall system strongly. Underwater Navigation methods can be divided into three main classes, Kalman filter-based, particle filter-based and graph-based, the advantages and disadvantages of each class are overviewed. In this paper, the authors present a performance comparison between three underwater navigation algorithms based on extend Kalman filter (EKF), particle filter (PF), incremental smoothing and mapping (iSAM). These approaches have been currently tested offline running on the experimental data collected with the Sailfish AUV during different missions on the bridge of Jiaozhou Bay and Menlou Reservoir. Different navigation algorithms have been tested by experimental data. The research's outcome has shown the advantage and disadvantage of these underwater navigation algorithms.