A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Jacob H. White and Randal W. Beard, "An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 942-953, July 2020. doi: 10.1109/JAS.2020.1003222
Citation: Jacob H. White and Randal W. Beard, "An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 942-953, July 2020. doi: 10.1109/JAS.2020.1003222

An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking

doi: 10.1109/JAS.2020.1003222
Funds:  This work was funded by the Center for Unmanned Aircraft Systems (C-UAS), a National Science Foundation Industry/University Cooperative Research Center (I/UCRC) under NSF award Numbers IIP-1161036 and CNS-1650547, along with significant contributions from C-UAS industry members
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  • This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit (IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking (MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/HhK-p2hXNnU.

     

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    Highlights

    • This paper introduces a new algorithm for estimating the relative pose of a moving camera.
    • A novel optimization algorithm solves for the relative pose using the epipolar constraint.
    • Applications include multi-target tracking, visual odometry, and 3D scene reconstruction.
    • If IMU information is available, it is used to seed the pose estimation algorithm.
    • Real-time execution of the algorithm is demonstrated on an embedded flight platform.

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