TY - GEN
T1 - A Kalman filter-based algorithm for IMU-camera calibration
AU - Mirzaei, Faraz M.
AU - Roumeliotis, Stergios I.
PY - 2007
Y1 - 2007
N2 - Vision-aided Inertial Navigation Systems (V-INS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera calibration process causes biases that reduce the accuracy of the estimation process and can even lead to divergence. In this paper, we present a Kalman filter-based algorithm for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlations of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3D laser scanner) except a calibration target. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.
AB - Vision-aided Inertial Navigation Systems (V-INS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera calibration process causes biases that reduce the accuracy of the estimation process and can even lead to divergence. In this paper, we present a Kalman filter-based algorithm for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlations of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3D laser scanner) except a calibration target. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.
UR - http://www.scopus.com/inward/record.url?scp=51349086794&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2007.4399342
DO - 10.1109/IROS.2007.4399342
M3 - Conference contribution
AN - SCOPUS:51349086794
SN - 1424409128
SN - 9781424409129
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2427
EP - 2434
BT - Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
T2 - 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Y2 - 29 October 2007 through 2 November 2007
ER -