Curtis Padgett
Jet Propulsion Laboratory, California Institute of Technology
Papers
6
Total Citations
322
H-Index
5
About
Curtis Padgett is a robotics and computer vision researcher whose work spans structural health monitoring, autonomous navigation, and planetary exploration. He first gained significant recognition with his 2011 methodology for crack detection and quantification using depth perception, a paper that has accumulated over 260 citations and demonstrated his early aptitude for applying sophisticated sensing techniques to real-world inspection challenges. In recent years, Padgett has pivoted toward the rapidly advancing field of autonomous off-road robotics, contributing foundational work on traversability estimation, long-range terrain prediction, and robust state estimation under degraded conditions. His RoadRunner framework addresses the formidable challenge of high-speed off-road navigation using onboard sensing alone, while complementary work on elevation mapping from imagery tackles the limitations of sparse LiDAR data at distance. His research on radar-based velocity factors further strengthens autonomy pipelines in environments where conventional sensors fail. Padgett has also extended his expertise beyond Earth, contributing to semantic mapping approaches for planetary robotic localization — critical for missions where GPS is unavailable. His DARE-SLAM work addresses resilience in perceptually challenging SLAM scenarios. Together, his portfolio reflects a researcher consistently pushing autonomous systems toward greater reliability in the world's most demanding environments.
Research Focus
Key Achievements
Top Papers
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