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Feature and Pose Constrained Visual Aided Inertial Navigation for Computationally Constrained Aerial VehiclesA 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.
Document ID
20120003288
Document Type
Conference Paper
External Source(s)
Authors
Williams, Brian (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Hudson, Nicolas (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Tweddle, Brent (Massachusetts Inst. of Tech. Cambridge, MA, United States)
Brockers, Roland (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Matthies, Larry (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 25, 2013
Publication Date
May 9, 2011
Subject Category
Aircraft Communications and Navigation
Meeting Information
2011 IEEE International Conference on Robotics and Automation(Shanghai)
Distribution Limits
Public
Copyright
Other
Keywords
autonomous operation
low-grade inertial sensors
micro air vehicles (MAV)