Papers
9
Total Citations
313
H-Index
7
About
Paul Ozog is a robotics researcher specializing in autonomous underwater vehicles (AUVs), simultaneous localization and mapping (SLAM), and computer vision for marine applications. His work sits at a compelling intersection of robotics, archaeology, and naval inspection, developing sophisticated systems that push the boundaries of what machines can perceive and navigate in challenging underwater environments. Ozog's most celebrated contribution — "High-Resolution Underwater Robotic Vision-Based Mapping and Three-Dimensional Reconstruction for Archaeology" (2016, 134 citations) — demonstrated how AUVs and diver-controlled stereo systems could document expansive underwater archaeological sites with unprecedented precision. Complementing this, his long-term SLAM research for ship hull inspection (2015, 75 citations) established robust navigation frameworks requiring minimal prior knowledge or acoustic aids, a significant practical advancement for maritime surveillance. Beyond underwater robotics, Ozog has contributed foundational work in real-time SLAM using piecewise-planar surface models, Bayesian estimation with high-dimensional features, and camera calibration uncertainty modeling — each addressing core challenges in mobile robotics perception. His research on identifying structural anomalies in hull reconstructions further demonstrates a commitment to translating theoretical advances into real-world inspection applications. With nearly 300 cumulative citations, Ozog has established himself as a thoughtful and impactful voice in autonomous underwater robotics research.
Research Focus
Key Achievements
Top Papers
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- 5On the importance of modeling camera calibration uncertainty in visual SLAM22 citations · 2013
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- 7Pose-Graph SLAM for Underwater Navigation10 citations · 2017
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- 9Robust visual fiducials for skin-to-skin relative ship pose estimation4 citations · 2016