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ABSTRACT: For small-scale Unmanned Aerial Vehicles (UAV) to operate indoor, in urban canyons or other scenarios where signals from global navigation satellite systems are denied or impaired, alternative estimation and control strategies must be applied. In this paper a system is proposed that estimates the self-motion and wind velocity by fusing information from airspeed sensors, an inertial measurement unit (IMU) and a monocular camera. Such estimates can be used in control systems for managing wind disturbances or chemical plume based tracking strategies. Simulation results indicate that while the inertial dead-reckoning process is subject to drift, the system is capable of separating the self-motion and wind velocity from the airspeed information.
Robotics and Automation (ICRA), 2011 IEEE International Conference on; 06/2011
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ABSTRACT: An estimation procedure for calibration of a low-cost inertial measurement unit (IMU), using a rigidly mounted monocular camera, is presented. The parameters of a sensor model that captures misalignments, scale and offset errors are estimated jointly with the IMU-camera coordinate transformation parameters using a recursive Sigma-Point Kalman Filter. The method requires only a simple visual calibration pattern. A simulation study indicates the filter's ability to reach subcentimeter and subdegree accuracy.
Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on; 10/2010
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ABSTRACT: In this paper, a simple method to calibrate the accelerometer cluster of an inertial measurement unit (IMU) is proposed. The method does not rely on using a mechanical calibration platform that rotates the IMU into different precisely controlled orientations. Although the IMU is rotated into different orientations, these orientations do not need to be known. Assuming that the IMU is stationary at each orientation, the norm of the input is considered equal to the gravity acceleration. As the orientations of the IMU are unknown, the calibration of the accelerometer cluster is stated as a blind system identification problem where only the norm of the input to the system is known. Under the assumption that the sensor noises have a white Gaussian distribution the system identification problem is solved using the maximum likelihood estimation method. The accuracy of the proposed calibration method is compared with the Cramér-Rao bound for the considered calibration problem.
Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on; 10/2010
