Papers
47
Total Citations
3,489
H-Index
20
About
Brendan Englot is a robotics researcher whose work spans autonomous navigation, simultaneous localization and mapping (SLAM), and intelligent exploration for mobile robots. He is perhaps best known as a co-creator of LIO-SAM, a landmark framework for tightly-coupled lidar-inertial odometry that formulates robot trajectory estimation atop a factor graph, enabling highly accurate real-time mapping — a contribution that has amassed nearly 2,000 citations and become a foundational reference in the robotics community. His research also makes significant strides in multi-robot systems, exemplified by DiSCo-SLAM, a distributed LiDAR SLAM framework enabling efficient collaboration among robot teams. Englot has made important contributions to autonomous underwater vehicles, developing advanced perception, navigation, and 3D coverage planning techniques for ship hull inspection — work that earned over 250 citations and helped establish underwater robotics as a rigorous field. His broader portfolio includes information-theoretic exploration, Gaussian process occupancy mapping, and deep reinforcement learning for autonomous navigation under uncertainty. Across these domains, Englot consistently bridges theoretical rigor with real-world applicability, making his work essential reading for students and researchers pursuing cutting-edge mobile robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping1,955 citations · 2020
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- 3Information-theoretic exploration with Bayesian optimization132 citations · 2016
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- 5LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping100 citations · 2020
- 6Simulation-based lidar super-resolution for ground vehicles83 citations · 2020
- 7
- 8Three-dimensional coverage planning for an underwater inspection robot80 citations · 2013
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