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Minimum Sigma Set SR-UKF for Quadrifocal Tensor-based Binocular Stereo Vision-IMU Tightly-coupled System

Published online by Cambridge University Press:  13 June 2018

Maosong Wang ,
Wenqi Wu ,
Naser El-Sheimy  and
Zhiwen Xian
Maosong Wang*
Affiliation:
(National University of Defense Technology, China) (The University of Calgary, Canada)
Wenqi Wu
Affiliation:
(National University of Defense Technology, China)
Naser El-Sheimy
Affiliation:
(The University of Calgary, Canada)
Zhiwen Xian
Affiliation:
(Taiyuan Satellite Launch Center, China)
*
(E-mail: [email protected])
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Abstract

This paper presents a binocular vision-IMU (Inertial Measurement Unit) tightly-coupled structure based on a Minimum sigma set Square-Root Unscented Kalman Filter (M-SRUKF) for real time navigation applications. Though the M-SRUKF has only half the sigma points of the SRUKF, it has the same accuracy as the SRUKF when applied to the binocular vision-IMU tightly-coupled system. As the Kalman filter flow is a kind of square-root system, the stability of the system can be guaranteed. The measurement model and the outlier rejection model of this tightly-coupled system not only utilises the epipolar constraint and the trifocal tensor geometry constraint between the consecutive two image pairs, but also uses the quadrifocal tensor geometry among four views. The structure of the binocular vision-IMU tightly-coupled system is in the form of an error state, and the time updates of the state and the state covariance are directly estimated without using Unscented Transformation (UT). Experiments are carried out based on an outdoor land vehicle open source dataset and an indoor Micro Aerial Vehicle (MAV) open source dataset. Results clearly show the effectiveness of the proposed new mechanisation.

Keywords

Minimum Sigma Set Square-Root Unscented Kalman FilterMultiple View GeometryQuadrifocal TensorBinocular Tightly-coupled Vision-IMU System
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 6 , November 2018 , pp. 1329 - 1343
Copyright
Copyright © The Royal Institute of Navigation 2018 

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