Inertial-Aided Metric States and Surface Normal Estimation using a Monocular Camera

Inertial-Aided Metric States and Surface Normal Estimation using a Monocular Camera In this paper, a visual inertial fusion framework is proposed for estimating the metric states of a Micro Aerial Vehicle (MAV) using optic flow (OF) and a homography model. Aided by the attitude estimation from the on-board Inertial Measurement Unit (IMU), the computed homography matrix is reshaped into a vector and directly fed into an Extend Kalman Filter (EKF). The sensor fusion method is able to recover metric distance, speed, acceleration bias and surface normal of the observed plane. We further consider reducing the size of the filter by using only part of the homography matrix as the system observation. Simulation results show that these smaller filters have reduced observability compared with the filter using the complete homography matrix, however it is still possible to estimate the metric states as long as one of the axes is linearly excited. Experiments using real sensory data show that our method is superior to the homography decomposition method for state and slope estimation. The proposed method is also validated in closed-loop flight tests of a quadrotor. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent & Robotic Systems Springer Journals

Inertial-Aided Metric States and Surface Normal Estimation using a Monocular Camera

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media Dordrecht
Subject
Engineering; Control, Robotics, Mechatronics; Electrical Engineering; Artificial Intelligence (incl. Robotics); Mechanical Engineering
ISSN
0921-0296
eISSN
1573-0409
D.O.I.
10.1007/s10846-017-0506-9
Publisher site
See Article on Publisher Site

Abstract

In this paper, a visual inertial fusion framework is proposed for estimating the metric states of a Micro Aerial Vehicle (MAV) using optic flow (OF) and a homography model. Aided by the attitude estimation from the on-board Inertial Measurement Unit (IMU), the computed homography matrix is reshaped into a vector and directly fed into an Extend Kalman Filter (EKF). The sensor fusion method is able to recover metric distance, speed, acceleration bias and surface normal of the observed plane. We further consider reducing the size of the filter by using only part of the homography matrix as the system observation. Simulation results show that these smaller filters have reduced observability compared with the filter using the complete homography matrix, however it is still possible to estimate the metric states as long as one of the axes is linearly excited. Experiments using real sensory data show that our method is superior to the homography decomposition method for state and slope estimation. The proposed method is also validated in closed-loop flight tests of a quadrotor.

Journal

Journal of Intelligent & Robotic SystemsSpringer Journals

Published: Feb 21, 2017

References

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