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Research Papers

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

[+] Author and Article Information
Trung Nguyen

Intelligent Systems Lab,
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X9, Canada
e-mail: tn0432@mun.ca

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: gmann@mun.ca

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: av@mun.ca

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: rgosine@mun.ca

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 141(8), 081012 (Mar 27, 2019) (10 pages) Paper No: DS-18-1461; doi: 10.1115/1.4042951 History: Received October 12, 2018; Revised February 16, 2019

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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Figures

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Fig. 3

Illustration of the 3D feature-point reconstruction

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Fig. 4

Trifocal tensor incidence relation (point-line-point) for three views I1, I2, and I3 with camera viewpoints O1, O2, and O3

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Fig. 2

System architecture of CKF implementation

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Fig. 7

Structure of TTG-based and 3D feature-point approach

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Fig. 10

Cubature Kalman filter estimation presented in Google map: (a) 2011_09_26_0095, (b) 2011_09_26_0036, and (c) 2011_09_30_0033

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Fig. 1

System coordinates

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Fig. 5

Illustration of the TTG based approach. The arrow is point transfer using TTG while the thin line is feature-tracking pipeline.

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Fig. 6

Example of point transfer using TTG with KITTI dataset. The point-line-point relation is established between the feature m1 in I1, the line l2 in I2 and the feature m3 in I3.

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Fig. 8

The predicted measurements of two approaches

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Fig. 9

Average processing time to predict one feature

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Fig. 11

Processing time evaluation of CKF and EKF

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Fig. 12

Experiments with EuRoC dataset

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Fig. 13

Cubature information filter system architecture in the general case of multiple measurement updates

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Fig. 15

Experimental results of dataset 2011_09_30_0020

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Fig. 16

Experimental results of dataset 2011_09_30_0033

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Fig. 17

RMSEpos evaluation of dataset 2011_09_30_0034

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Fig. 18

RMSEpos evaluation of dataset 2011_09_30_0020

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Fig. 19

RMSEpos evaluation of dataset 2011_09_30_0033

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Fig. 20

Processing time evaluation of CKF and CIF

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Fig. 14

Experimental results of dataset 2011_09_30_0034

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