Sensor Fusion Using Error-State Kalman Filter to Improve Localization of Autonomous Underwater Vehicle Under DVL Signal Loss

This paper concerns the development and improvement of the underwater navigation system for Autonomous Underwater Vehicles (AUVs) when faced with velocity-aiding sensor failures. The study addresses this challenge of sensor fusion by applying the Error-State Kalman Filter (ESKF), a form of indirect state filtering. Specifically, the ESKF targets the limitations of velocity measurements encountered during near-bottom operations. The proposed method was applied to the Xplorer-Mini AUV and evaluated using the Gazebo physics engine simulator in ROS 2. To assess its performance in handling the loss of velocity signals, a series of simulation-based experiments were conducted and compared against the traditional Inertial Navigation System (INS) and Extended Kalman Filter (EKF) algorithms. The results of the experiments demonstrate that the ESKF outperforms traditional INS and EKF algorithms, offering valuable insights into state estimation techniques for developing autonomous underwater vehicles in challenging environments.