Papers
15
Total Citations
813
H-Index
9
About
Jason Williams is a leading roboticist whose research focuses on scalable multitarget tracking, simultaneous localization and mapping (SLAM), and multi-robot autonomy for extreme environments. He is best known for his pioneering work on the DARPA Subterranean (SubT) Challenge, where he led Team CSIRO Data41 to a top score by developing heterogeneous teams of ground and aerial robots that could autonomously explore dangerous underground tunnels, caves, and urban infrastructure. His 2018 tutorial on message passing algorithms for scalable multitarget tracking (347 citations) has become a foundational reference for autonomous driving, indoor localization, and crowd counting. Williams has also advanced SLAM in extreme environments, with his 2023 survey (176 citations) charting the future of subterranean exploration from Earth to Mars. His practical innovations include GPU-based occupancy mapping for real-time 3D lidar processing and canopy density estimation for precision agriculture using 3D spinning lidar SLAM. With over 800 total citations, Williams’s work bridges theoretical algorithms and field-deployed systems, demonstrating how robot teams can operate in GPS-denied, unstructured settings. His contributions have been recognized through leadership of award-winning DARPA teams and publications in top robotics venues, making him a key figure in the push toward resilient, autonomous exploration.
Research Focus
Key Achievements
Top Papers
- 1Message Passing Algorithms for Scalable Multitarget Tracking347 citations · 2018
- 2Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge176 citations · 2023
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- 6Present and Future of SLAM in Extreme Underground Environments38 citations · 2022
- 7OHM: GPU Based Occupancy Map Generation17 citations · 2022
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