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About

Joshua G. Mangelson is a robotics researcher whose work spans autonomous navigation, state estimation, and multi-robot systems, with particular expertise in simultaneous localization and mapping (SLAM), underwater robotics, and tactile perception. His most influential contribution, "Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging" (2018, 195 citations), introduced a groundbreaking method for reliably merging maps across multiple robots without requiring prior initialization — a long-standing challenge in collaborative SLAM. His development of the HoloOcean simulator suite has become an essential resource for the underwater robotics community, providing high-fidelity acoustic sonar modeling and full marine environment simulation that dramatically reduce the cost and complexity of algorithm development. Mangelson has also made notable contributions to underwater navigation through Invariant Extended Kalman Filtering (91 citations), improving localization accuracy using Lie group theory, and to subterranean exploration through multi-robot teaming. His more recent work pushes into tactile sensing and 3D shape mapping, combining touch and vision for dexterous manipulation in unstructured environments. Collectively, his research reflects a drive to make autonomous robots more resilient and capable across some of the most demanding real-world settings imaginable.