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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2014/41949
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| Title: | Feature and pose constrained visual aided inertial navigation for computationally constrained aerial vehicles |
| Authors: | Williams, Brian
Hudson, Nicolas
Tweddle, Brent
Brockers, Roland
Matthies, Larry |
| Keywords: | autonomous operation
micro air vehicles (MAV)
low-grade inertial sensors |
| Issue Date: | 9-May-2011 |
| Publisher: | Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011. |
| Citation: | IEEE International Conference on Robotics and Automation, Shanghai, China, May 9-13, 2011. |
| Abstract: | A Feature and Pose Constrained Extended Kalman Filter (FPC-EKF) is developed for highly dynamic computationally constrained micro aerial vehicles. Vehicle localization is achieved using only a low performance inertial measurement unit and a single camera. The FPC-EKF framework augments the vehicle’s state with both previous vehicle poses and critical environmental features, including vertical edges. This filter framework efficiently incorporates measurements from hundreds of opportunistic visual features to constrain the motion estimate, while allowing navigating and sustained tracking with respect to a few persistent features. In addition, vertical features in the environment are opportunistically used to provide global attitude references. Accurate pose estimation is demonstrated on a sequence including fast traversing, where visual features enter and exit the field-of-view quickly, as well as hover and ingress maneuvers where drift free navigation is achieved with respect to the environment. |
| URI: | http://hdl.handle.net/2014/41949 |
| Appears in Collections: | JPL TRS 1992+
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