System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Miniaturization, Calibration & Accuracy Evaluation of a Hybrid Self-Tracker
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
An Introduction to Inertial and Visual Sensing
International Journal of Robotics Research
Relative Pose Calibration Between Visual and Inertial Sensors
International Journal of Robotics Research
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Relative pose calibration of a spherical camera and an IMU
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
IEEE Transactions on Robotics
Gyro-aided feature tracking for a moving camera: fusion, auto-calibration and GPU implementation
International Journal of Robotics Research
3D hand tracking for human computer interaction
Image and Vision Computing
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This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The method is based on a physical model which can also be used in solving, for example, sensor fusion problems. The experimental results show that the method works well in practice, both for perspective and spherical cameras.