Ali‐akbar Agha‐mohammadi
Jet Propulsion Laboratory, California Institute of Technology, Decision Systems (United States), Qualcomm (United States), National Aeronautics and Space Administration, Georgia Institute of Technology, Massachusetts Institute of Technology, University of Southern California, Mission Heritage Medical Group, Texas A&M University, Fielding Graduate University, DEVCOM Army Research Laboratory
Papers
91
Total Citations
2,411
H-Index
27
About
Ali-akbar Agha-mohammadi is a leading robotics researcher whose work sits at the intersection of autonomous navigation, simultaneous localization and mapping (SLAM), and multi-robot systems, with a particular focus on operating in extreme and perceptually-degraded environments. He is best known for his pivotal contributions to the DARPA Subterranean Challenge, where his team developed landmark systems including LAMP and its successor LAMP 2.0 — robust SLAM frameworks enabling heterogeneous robot teams to map and navigate complex underground environments such as tunnels, caves, and disaster zones. These works have collectively garnered over 340 citations, underscoring their significance to the field. His Direct LiDAR Odometry and LOCUS 2.0 systems address the computational demands of real-time localization with dense point clouds, while his STEP framework brings principled risk-aware planning to off-road autonomous navigation. Agha-mohammadi has also pioneered sensor fusion approaches using millimeter-wave radar and IMUs for vision-denied settings, and explored multimodal legged-aerial robotic platforms for search-and-rescue missions. Spanning from theoretical foundations in decentralized partially observable decision-making to field-hardened deployments, his research continues to push the boundaries of what autonomous robots can achieve in the world's most challenging environments.
Research Focus
Key Achievements
Top Papers
- 1Direct LiDAR Odometry: Fast Localization With Dense Point Clouds205 citations · 2022
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- 3Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge176 citations · 2023
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- 6Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments102 citations · 2020
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