Localization of an underwater robot for remote data collection

Underwater localization is a fundamental requirement in underwater robotics. Accurate and drift free pose estimates are required for navigation and operation in challenging underwater environments. Inaccuracies and errors in localization would result in the degradation of the data collected. In this work, a low-cost localization method using the inertial measurement unit (IMU) and an Extended Kalman Filter (EKF) algorithm is proposed for miniature underwater robots. The underwater robot used in this work is the open source Remotely Operated Vehicle (openROV). The proposed method uses data from the IMU of the openROV robot and inputs it into the EKF algorithm to create a map which shows the position and path of the openROV robot. The fundamental algorithmic principles behind the localization technique is described, and the algorithm is tested using a dataset and a simulation. Simulation results show that the designed algorithm can achieve accurate pose estimation.Bachelor of Engineering (Electrical and Electronic Engineering