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Address:
Jet Propulsion Laboratory M/S 198-235
4800 Oak Grove Drive
Pasadena, CA 91109 |
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Phone:
818.393.7050 |
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Fax:
818.393.5007 |
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Email:
Click here
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Member of:
Former JPL Robotics Personnel |
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FORMER MEMBER
Dr Andrew Howard
Member of Technical Staff
(Short description>>)
Dr. Howard is a Senior Member of Technical Staff with the Computer Vision Group at JPL, and an Adjunct Assistant Professor at the University of Southern California. Prior to joining JPL, he was a Research Assistant Professor at the USC Center for Robotics and Embedded Systems and USC Robotics Research Laboratory.
Dr. Howard received his Ph.D. in Engineering from the University of Melbourne in 1999, and his B.Sc. (Hons.) in Theoretical Physics from the University of Melbourne in 1991.
Dr. Howard has extensive experience with a wide range of terrestrial robotics programs, including DARPA BigDog, Crusher and Urban Grand Challenge (PI or technical lead for the perception sub-systems), ARL Robotics Collaborative Technology Alliance SafeOps (human detection lead), and the Navy/ONR Unmanned Surface Vessel programs (perception lead).
Dr. Howard is a member of the Editorial Boards for the Journal of Field Robotics and the International Journal of Robotics Research; he has also served on the program committees of many robotics conferences, including ICRA, IROS, FSR and RSS.
Research Tasks:
Long-range Autonomous Rover Localization
Rapid 3D Object Visualization For Mobile Robots
Vision System Development for BigDog
Dr. Howards research interests include vision-based localization and mapping, visual odometry, visual SLAM, stereo ranging, object detection and tracking, and estimation and control of multi-robot systems.
NASA Board Awards for NTR 46532 "JPL Robot Vision Library (JPLV)", NTR 42336 "Survey Free Camera Calibration".
NASA Tech Brief Awards for NTR 46601 "Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors", NTR 46114 "Visual SLAM in Unstructured Environments using Variance Grid Maps (Gamma-SLAM)".
Team Bonus Award for "Intelligent Autonomy for Navy USV".
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