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Sensors 2015, 15(6), 12816-12833; doi:10.3390/s150612816

Tightly-Coupled Stereo Visual-Inertial Navigation Using Point and Line Features

College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China
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Academic Editor: Vittorio M.N. Passaro
Received: 13 April 2015 / Accepted: 27 May 2015 / Published: 1 June 2015
(This article belongs to the Section Physical Sensors)
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Abstract

This paper presents a novel approach for estimating the ego-motion of a vehicle in dynamic and unknown environments using tightly-coupled inertial and visual sensors. To improve the accuracy and robustness, we exploit the combination of point and line features to aid navigation. The mathematical framework is based on trifocal geometry among image triplets, which is simple and unified for point and line features. For the fusion algorithm design, we employ the Extended Kalman Filter (EKF) for error state prediction and covariance propagation, and the Sigma Point Kalman Filter (SPKF) for robust measurement updating in the presence of high nonlinearities. The outdoor and indoor experiments show that the combination of point and line features improves the estimation accuracy and robustness compared to the algorithm using point features alone. View Full-Text
Keywords: vision-aided inertial navigation; point and line features; trifocal geometry; tightly-coupled vision-aided inertial navigation; point and line features; trifocal geometry; tightly-coupled
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kong, X.; Wu, W.; Zhang, L.; Wang, Y. Tightly-Coupled Stereo Visual-Inertial Navigation Using Point and Line Features. Sensors 2015, 15, 12816-12833.

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