Papers
12
Total Citations
203
H-Index
7
About
Clark N. Taylor is a leading researcher in robotics and state estimation, with key contributions spanning cooperative localization, 3D pose estimation, and robust sensor fusion. His seminal work on bearing-only cooperative localization, including the highly cited 2011 paper (91 citations), introduced a graph-based observability analysis that links measurement topology to system observability—a foundational insight for multi-robot teams operating with limited sensing. Taylor’s research also addresses critical challenges in iterative closest point (ICP) algorithms, accelerating nearest-neighbor search with the Delaunay walk (14 citations) and providing accurate covariance estimation for pose data (11 citations). He has advanced robust localization under non-line-of-sight conditions, developing factor-graph-based error estimation and covariance adaptation techniques that enable reliable state estimation in safety-critical applications like autonomous urban navigation. His work on 6D pose estimation using YOLOv5 (7 citations) further demonstrates his impact on perception for robotics and close-contact aircraft operations. With over 200 total citations, Taylor’s research bridges theoretical rigor and practical deployment, making him a key figure in enabling robust, real-time robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Graph-Based Observability Analysis of Bearing-Only Cooperative Localization91 citations · 2011
- 2Bearing-only Cooperative Localization50 citations · 2013
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- 6Covariance Estimation for Factor Graph Based Bayesian Estimation7 citations · 2020
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- 8Batch Measurement Error Covariance Estimation for Robust Localization4 citations · 2018
- 9
- 10Robust Incremental State Estimation Through Covariance Adaptation3 citations · 2020