Papers
54
Total Citations
4,278
H-Index
28
About
Ryan M. Eustice is a prominent roboticist whose research spans simultaneous localization and mapping (SLAM), autonomous underwater vehicles, autonomous driving, and state estimation. Best known for pioneering sparse information-theoretic approaches to SLAM, Eustice demonstrated that SLAM information matrices are *exactly* sparse in delayed-state frameworks — a foundational insight that dramatically improved scalability for large-scale mapping problems, as detailed in his highly cited 2006 and 2007 papers (collectively exceeding 600 citations). His underwater robotics work is particularly distinguished: he developed vision-based navigation systems capable of mapping the RMS Titanic and ancient shipwrecks off Chios, Greece, pushing the boundaries of autonomous operation in GPS-denied, acoustically challenging environments. His 2012 work on autonomous ship hull inspection (251 citations) further cemented his reputation in marine robotics. On land, his 2015 University of Michigan North Campus dataset (548 citations) became a benchmark resource for long-term autonomy research. More recently, his work on contact-aided invariant extended Kalman filtering (295 citations) has advanced legged robot state estimation. Across more than a decade of contributions, Eustice's research has profoundly shaped how autonomous systems perceive, navigate, and map complex real-world environments.
Research Focus
Key Achievements
Top Papers
- 1University of Michigan North Campus long-term vision and lidar dataset548 citations · 2015
- 2Exactly Sparse Delayed-State Filters for View-Based SLAM298 citations · 2006
- 3Contact-aided invariant extended Kalman filtering for robot state estimation295 citations · 2020
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- 6Exactly Sparse Extended Information Filters for Feature-based SLAM202 citations · 2007
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- 8Visually Navigating the RMS Titanic with SLAM Information Filters182 citations · 2005
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- 10Exactly Sparse Delayed-State Filters147 citations · 2006