Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints

Guan Banglei, Yu Qifeng, Friedrich Fraundorfer

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In this paper, we explore the different minimal case solutions to the rotational alignment of IMU-camera systems using homography constraints. The assumption that a ground plane is visible in the images can easily be created in many situations. This calibration process is relevant to many smart devices equipped with a camera and an inertial measurement unit (IMU), like micro aerial vehicles (MAVs), smartphones and tablets, and it is a fundamental step for vision and IMU data fusion. Our solutions are novel as they compute the rotational alignment of IMU-camera systems by utilizing a first-order rotation approximation and by solving a polynomial equation system derived from homography constraints. These solutions depend on the calibration case with respect to camera motion (general motion case or pure rotation case) and camera parameters (calibrated camera or partially uncalibrated camera). We then demonstrate that the number of matched points in an image pair can vary from 1.5 to 3. This enables us to calibrate using only one relative movement and provide the exact algebraic solution to the problem. The novel minimal case solutions are useful to reduce the computation time and increase the calibration robustness when using Random Sample Consensus (RANSAC) on the point correspondences between two images. Furthermore, a non-linear parameter optimization over all image pairs is performed. In contrast to the previous calibration methods, our solutions do not require any special hardware, and no problems are experienced with one image pair without special motion. Finally, by evaluating our algorithm on both synthetic and real scene data including data obtained from robots, smartphones and MAVs, we demonstrate that our methods are both efficient and numerically stable for the rotational alignment of IMU-camera systems.
Original languageEnglish
Pages (from-to)79 - 91
JournalComputer vision and image understanding
Publication statusPublished - 2018

Keywords

    Cite this

    Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints. / Banglei, Guan; Qifeng, Yu; Fraundorfer, Friedrich.

    In: Computer vision and image understanding, 2018, p. 79 - 91.

    Research output: Contribution to journalArticleResearchpeer-review

    @article{b8128e8d0446461ebcd929a86d67e955,
    title = "Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints",
    abstract = "In this paper, we explore the different minimal case solutions to the rotational alignment of IMU-camera systems using homography constraints. The assumption that a ground plane is visible in the images can easily be created in many situations. This calibration process is relevant to many smart devices equipped with a camera and an inertial measurement unit (IMU), like micro aerial vehicles (MAVs), smartphones and tablets, and it is a fundamental step for vision and IMU data fusion. Our solutions are novel as they compute the rotational alignment of IMU-camera systems by utilizing a first-order rotation approximation and by solving a polynomial equation system derived from homography constraints. These solutions depend on the calibration case with respect to camera motion (general motion case or pure rotation case) and camera parameters (calibrated camera or partially uncalibrated camera). We then demonstrate that the number of matched points in an image pair can vary from 1.5 to 3. This enables us to calibrate using only one relative movement and provide the exact algebraic solution to the problem. The novel minimal case solutions are useful to reduce the computation time and increase the calibration robustness when using Random Sample Consensus (RANSAC) on the point correspondences between two images. Furthermore, a non-linear parameter optimization over all image pairs is performed. In contrast to the previous calibration methods, our solutions do not require any special hardware, and no problems are experienced with one image pair without special motion. Finally, by evaluating our algorithm on both synthetic and real scene data including data obtained from robots, smartphones and MAVs, we demonstrate that our methods are both efficient and numerically stable for the rotational alignment of IMU-camera systems.",
    keywords = "IMU-camera calibration, Rotational alignment, Minimal solution, Homography constraint, Algebraic solution, Pure rotation",
    author = "Guan Banglei and Yu Qifeng and Friedrich Fraundorfer",
    year = "2018",
    language = "English",
    pages = "79 -- 91",
    journal = "Computer vision and image understanding",
    issn = "1077-3142",
    publisher = "Academic Press",

    }

    TY - JOUR

    T1 - Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints

    AU - Banglei, Guan

    AU - Qifeng, Yu

    AU - Fraundorfer, Friedrich

    PY - 2018

    Y1 - 2018

    N2 - In this paper, we explore the different minimal case solutions to the rotational alignment of IMU-camera systems using homography constraints. The assumption that a ground plane is visible in the images can easily be created in many situations. This calibration process is relevant to many smart devices equipped with a camera and an inertial measurement unit (IMU), like micro aerial vehicles (MAVs), smartphones and tablets, and it is a fundamental step for vision and IMU data fusion. Our solutions are novel as they compute the rotational alignment of IMU-camera systems by utilizing a first-order rotation approximation and by solving a polynomial equation system derived from homography constraints. These solutions depend on the calibration case with respect to camera motion (general motion case or pure rotation case) and camera parameters (calibrated camera or partially uncalibrated camera). We then demonstrate that the number of matched points in an image pair can vary from 1.5 to 3. This enables us to calibrate using only one relative movement and provide the exact algebraic solution to the problem. The novel minimal case solutions are useful to reduce the computation time and increase the calibration robustness when using Random Sample Consensus (RANSAC) on the point correspondences between two images. Furthermore, a non-linear parameter optimization over all image pairs is performed. In contrast to the previous calibration methods, our solutions do not require any special hardware, and no problems are experienced with one image pair without special motion. Finally, by evaluating our algorithm on both synthetic and real scene data including data obtained from robots, smartphones and MAVs, we demonstrate that our methods are both efficient and numerically stable for the rotational alignment of IMU-camera systems.

    AB - In this paper, we explore the different minimal case solutions to the rotational alignment of IMU-camera systems using homography constraints. The assumption that a ground plane is visible in the images can easily be created in many situations. This calibration process is relevant to many smart devices equipped with a camera and an inertial measurement unit (IMU), like micro aerial vehicles (MAVs), smartphones and tablets, and it is a fundamental step for vision and IMU data fusion. Our solutions are novel as they compute the rotational alignment of IMU-camera systems by utilizing a first-order rotation approximation and by solving a polynomial equation system derived from homography constraints. These solutions depend on the calibration case with respect to camera motion (general motion case or pure rotation case) and camera parameters (calibrated camera or partially uncalibrated camera). We then demonstrate that the number of matched points in an image pair can vary from 1.5 to 3. This enables us to calibrate using only one relative movement and provide the exact algebraic solution to the problem. The novel minimal case solutions are useful to reduce the computation time and increase the calibration robustness when using Random Sample Consensus (RANSAC) on the point correspondences between two images. Furthermore, a non-linear parameter optimization over all image pairs is performed. In contrast to the previous calibration methods, our solutions do not require any special hardware, and no problems are experienced with one image pair without special motion. Finally, by evaluating our algorithm on both synthetic and real scene data including data obtained from robots, smartphones and MAVs, we demonstrate that our methods are both efficient and numerically stable for the rotational alignment of IMU-camera systems.

    KW - IMU-camera calibration

    KW - Rotational alignment

    KW - Minimal solution

    KW - Homography constraint

    KW - Algebraic solution

    KW - Pure rotation

    M3 - Article

    SP - 79

    EP - 91

    JO - Computer vision and image understanding

    JF - Computer vision and image understanding

    SN - 1077-3142

    ER -