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Scale robust IMU-assisted KLT for stereo visual odometry solution

Published online by Cambridge University Press:  30 August 2016

L. Chermak Open the ORCID record for L. Chermak [Opens in a new window] ,
N. Aouf  and
M. A. Richardson
L. Chermak*
Affiliation:
Centre of Electronic Warfare, Cranfield University, Shrivenham, SN6 8LA, UK E-mails: [email protected], [email protected]
N. Aouf
Affiliation:
Centre of Electronic Warfare, Cranfield University, Shrivenham, SN6 8LA, UK E-mails: [email protected], [email protected]
M. A. Richardson
Affiliation:
Centre of Electronic Warfare, Cranfield University, Shrivenham, SN6 8LA, UK E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

We propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context.

Keywords

Computer visionNavigationIMU-KLT feature trackingVisual odometryDouble dogleg
Type
Articles
Information
Robotica , Volume 35 , Issue 9 , September 2017 , pp. 1864 - 1887
Copyright
Copyright © Cambridge University Press 2016 

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