Papers
119
Total Citations
3,788
H-Index
26
About
Timothy D. Barfoot is a leading robotics researcher whose work spans state estimation, autonomous navigation, motion planning, and mobile robot control. He is perhaps best known for his foundational textbook *State Estimation for Robotics* (2017), which has become an essential reference in the field with over 730 citations, offering students and practitioners a rigorous treatment of how robots estimate position and orientation from noisy sensor data — a problem central to virtually all autonomous systems. His contributions to motion planning are equally significant, co-developing Batch Informed Trees (BIT*), an elegant algorithm unifying sampling- and graph-based planning that has garnered over 400 citations and influenced modern path-planning research worldwide. Barfoot has also made substantial advances in learning-based Nonlinear Model Predictive Control (LB-NMPC), enabling mobile robots to reliably track paths across challenging off-road terrain through adaptive disturbance learning — work that collectively spans hundreds of citations across multiple publications. His research on multi-robot cooperative localization, decentralized state estimation, and long-term visual teach-and-repeat navigation further demonstrates a sustained commitment to making robots robust in real-world environments. Through rigorous theory, open datasets, and practical algorithms, Barfoot has profoundly shaped how autonomous robots perceive, plan, and navigate the physical world.
Research Focus
Key Achievements
Top Papers
- 1State Estimation for Robotics734 citations · 2017
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- 5State Estimation for Robotics137 citations · 2024
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- 7The UTIAS multi-robot cooperative localization and mapping dataset110 citations · 2011
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