This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.
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August 2019
Research-Article
Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry Available to Purchase
Trung Nguyen,
Trung Nguyen
Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
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George K. I. Mann,
George K. I. Mann
Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
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Andrew Vardy,
Andrew Vardy
Department of Computer Science;
Department of Electrical and Computer Engineering,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Department of Electrical and Computer Engineering,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
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Raymond G. Gosine
Raymond G. Gosine
Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Search for other works by this author on:
Trung Nguyen
Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
George K. I. Mann
Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Andrew Vardy
Department of Computer Science;
Department of Electrical and Computer Engineering,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Department of Electrical and Computer Engineering,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Raymond G. Gosine
Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: [email protected]
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received October 12, 2018; final manuscript received February 16, 2019; published online March 27, 2019. Assoc. Editor: Richard Bearee.
J. Dyn. Sys., Meas., Control. Aug 2019, 141(8): 081012 (10 pages)
Published Online: March 27, 2019
Article history
Received:
October 12, 2018
Revised:
February 16, 2019
Citation
Nguyen, T., Mann, G. K. I., Vardy, A., and Gosine, R. G. (March 27, 2019). "Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry." ASME. J. Dyn. Sys., Meas., Control. August 2019; 141(8): 081012. https://doi.org/10.1115/1.4042951
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