Autonomous underwater vehicles (AUVs) have the potential to dramatically improve safety, quality of life and general scientific knowledge. Our coasts, lakes and rivers are filled with various forms of marine infrastructure including dams, bridges, ship hulls, communication lines, and oil rigs. Each of these structures requires regular inspection, and current methods utilize divers, which is dangerous, expensive, and time consuming. AUVs have the potential to alleviate these difficulties and enable more regular inspection of these structures. Furthermore, there are significant scientific discoveries in the fields of geology, marine biology and medicine that AUV exploration of our oceans will enable. Since field trials of AUVs can be both expensive and high-risk, making a simulation method to generate data for algorithm development is a necessity. For this purpose, we present HoloOcean, an open-source, fully-featured, underwater robotics simulator. Built upon Unreal Engine 4 (UE4), HoloOcean comes equipped with multi-agent communications, common underwater sensors, high-fidelity graphics, and a novel sonar simulation method. Our novel sonar simulation framework is built upon an octree structure, allowing for rapid data generation and flexible usage to simulate a variety of sonars. Further, we have augmented this simulation to incorporate various probabilistic models to account for the heavy noise found in sonar imagery. Simulation enables development of many algorithms such as mapping, localization, structure from motion, controls, and many others. Localization is one essential algorithm for AUV navigation. Recent developments in the utilization of Lie Groups for robotic localization have lead to dramatic performance improvements in convergence and uncertainty characterization. One such method, the Invariant Extended Kalman Filter (InEKF), leverages that invariant error dynamics defined on matrix Lie Groups satisfy a log-linear differential equation. We lay out the various practical decisions required for the InEKF, and show that the primary sensors used in underwater robotics with minor modifications can be used in the InEKF. We show the convergence improvements of the InEKF over the quaternion-based extended Kalman filter (QEKF) on HoloOcean data, both in low and high uncertainty scenarios.



College and Department

Ira A. Fulton College of Engineering; Electrical and Computer Engineering



Date Submitted


Document Type





marine robotics, simulation, sonar, Lie group, invariant, Kalman filter



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Engineering Commons