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Vision-only egomotion estimation in 6DOF using a sky compass

Published online by Cambridge University Press:  25 July 2018

Tasarinan Jouir ,
Reuben Strydom ,
Thomas M. Stace  and
Mandyam V. Srinivasan
Tasarinan Jouir*
Affiliation:
Queensland Brain Institute, QBI Building 79, University of Queensland, St Lucia, Brisbane, Queensland, Australia, 4072
Reuben Strydom
Affiliation:
Queensland Brain Institute, QBI Building 79, University of Queensland, St Lucia, Brisbane, Queensland, Australia, 4072 School of Information Technology and Electrical Engineering, General Purpose South Building 78, University of Queensland, St Lucia, Brisbane, Queensland, Australia, 4072 E-mails: reuben.strydom@uqconnect.edu.au, m.srinivasan@uq.edu.au
Thomas M. Stace
Affiliation:
School of Mathematics and Physics, Parnell Building 07, University of Queensland, St Lucia, Brisbane, Queensland, Australia, 4072. E-mail: stace@physics.uq.edu.au
Mandyam V. Srinivasan
Affiliation:
Queensland Brain Institute, QBI Building 79, University of Queensland, St Lucia, Brisbane, Queensland, Australia, 4072 School of Information Technology and Electrical Engineering, General Purpose South Building 78, University of Queensland, St Lucia, Brisbane, Queensland, Australia, 4072 E-mails: reuben.strydom@uqconnect.edu.au, m.srinivasan@uq.edu.au
*
*Corresponding author. E-mail: tas.jouir@uq.net.au
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Summary

A novel pure-vision egomotion estimation algorithm is presented, with extensions to Unmanned Aerial Systems (UAS) navigation through visual odometry. Our proposed method computes egomotion in two stages using panoramic images segmented into sky and ground regions. Rotations (in 3DOF) are estimated by using a customised algorithm to measure the motion of the sky image, which is affected only by the rotation of the aircraft, and not by its translation. The rotation estimate is then used to derotate the optic flow field generated by the ground, from which the translation of the aircraft (in 3DOF) is estimated by another customised, iterative algorithm. Segmentation of the rotation and translation estimations allows for a partial relaxation of the planar ground assumption, inherently increasing the robustness of the approach. The translation vectors are scaled using stereo-based height to compute the current UAS position through path integration for closed-loop navigation. Outdoor field tests of our approach in a small quadrotor UAS suggest that the technique is comparable to the performance of existing state-of-the-art vision-based navigation algorithms, whilst also removing all dependence on additional sensors, such as an IMU or global positioning system (GPS).

Keywords

NavigationEgomotionPath IntegrationOptic FlowSky Compass
Type
Articles
Information
Robotica , Volume 36 , Issue 10 , October 2018 , pp. 1571 - 1589
Copyright
Copyright © Cambridge University Press 2018 

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