TY - GEN
T1 - Precise input and output error characterization for loosely integrated INS/GPS/camera navigation system
AU - Lemay, Lee
AU - Chu, Chen Chi
AU - Gebre Egziabher, Demoz
AU - Ramllal, Rohan
PY - 2011
Y1 - 2011
N2 - A method for the loose integration of INS, GNSS and cameras using an Extended Kalman Filter (EKF) is presented. The integration is loose because it occurs at the level of position, velocity and attitude. The GNSS supplies position and velocity estimates (when available) and the camera provides position and attitude estimates. The information provided by GNSS and the camera are used in the EKF to estimate inertial sensor errors and, thus, mitigate INS drift. Since integration via an EKF requires accurate characterization of errors (used as weights in the calculation of gains), a method for estimating camera position and attitude covariance as function of pixel noise is presented. A series of Monte Carlo simulations show that the proposed approach estimates the camera position covariance well but underestimates the attitude covariance. Simulation results showing the performance of an integrated INS/GNSS/Camera system using the developed covariance estimator is presented. The results show that an integration with 1 Hz camera position/attitude updates is sufficient for estimating the inertial sensor errors of an INS which uses an automotive grade IMU.
AB - A method for the loose integration of INS, GNSS and cameras using an Extended Kalman Filter (EKF) is presented. The integration is loose because it occurs at the level of position, velocity and attitude. The GNSS supplies position and velocity estimates (when available) and the camera provides position and attitude estimates. The information provided by GNSS and the camera are used in the EKF to estimate inertial sensor errors and, thus, mitigate INS drift. Since integration via an EKF requires accurate characterization of errors (used as weights in the calculation of gains), a method for estimating camera position and attitude covariance as function of pixel noise is presented. A series of Monte Carlo simulations show that the proposed approach estimates the camera position covariance well but underestimates the attitude covariance. Simulation results showing the performance of an integrated INS/GNSS/Camera system using the developed covariance estimator is presented. The results show that an integration with 1 Hz camera position/attitude updates is sufficient for estimating the inertial sensor errors of an INS which uses an automotive grade IMU.
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M3 - Conference contribution
AN - SCOPUS:79956322597
SN - 9781617823985
T3 - Institute of Navigation - International Technical Meeting 2011, ITM 2011
SP - 880
EP - 894
BT - Institute of Navigation - International Technical Meeting 2011, ITM 2011
T2 - Institute of Navigation - International Technical Meeting 2011, ITM 2011
Y2 - 24 January 2011 through 26 January 2011
ER -