ICRA 2011 Paper Abstract

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Paper TuA114.3

Williams, Brian Patrick (Jet Propulsion Laboratory), Hudson, Nicolas Henry (Jet Propulsion Laboratory), Tweddle, Brent Edward (Massachusetts Institute of Technology), Brockers, Roland (California Institute of Technology), Matthies, Larry (California Institute of Technology)

Feature and Pose Constrained Visual Aided Inertial Navigation for Computationally Constrained Aerial Vehicles

Scheduled for presentation during the Regular Sessions "Visual Navigation I" (TuA114), Tuesday, May 10, 2011, 08:50−09:05, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 16, 2019

Keywords Visual Navigation, Localization, Aerial Robotics

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.

 

 

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