Papers
39
Total Citations
1,176
H-Index
15
About
Jonathan Kelly is a leading researcher in robot perception, navigation, and sensor fusion, with a particular focus on visual-inertial systems. His seminal work on visual-inertial sensor fusion, including the highly cited 2010 paper (530 citations), established foundational methods for accurate localization, mapping, and sensor-to-sensor self-calibration—critical for enabling robust robot navigation in GPS-denied environments. Kelly’s contributions extend to temporal calibration of multi-sensor systems (86 citations), combined visual-inertial navigation for unmanned aerial vehicles (75 citations), and distributed collaborative SLAM, where his work on budgeted data exchange for loop closure detection (27 citations) addresses resource-efficient multi-robot mapping. He has also advanced manipulation through learning task error models (34 citations) and inverse kinematics via convex iteration (27 citations). Notable achievements include his work on canonical appearance transformations for direct visual localization under illumination change (26 citations) and robust monocular visual teach and repeat aided by ground planarity (24 citations). With over 900 total citations across his most-cited papers, Kelly’s research has profoundly impacted autonomous systems, enabling more reliable, self-calibrating robots for aerial, ground, and manipulation tasks.
Research Focus
Key Achievements
Top Papers
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- 4Combined Visual and Inertial Navigation for an Unmanned Aerial Vehicle75 citations · 2008
- 5Learning task error models for manipulation34 citations · 2013
- 6Convex Iteration for Distance-Geometric Inverse Kinematics27 citations · 2022
- 7Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection27 citations · 2018
- 8AN EXPERIMENTAL STUDY OF AERIAL STEREO VISUAL ODOMETRY26 citations · 2007
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