Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance EvaluationReport as inadecuate




Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation - Download this document for free, or read online. Document in PDF available to download.

The Institute of BioRobotics, Scuola Superiore SantAnna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy





*

Author to whom correspondence should be addressed.



Abstract In this paper measurements from a monocular vision system are fused with inertial-magnetic measurements from an Inertial Measurement Unit IMU rigidly connected to the camera. Two Extended Kalman filters EKFs were developed to estimate the pose of the IMU-camera sensor moving relative to a rigid scene ego-motion, based on a set of fiducials. The two filters were identical as for the state equation and the measurement equations of the inertial-magnetic sensors. The DLT-based EKF exploited visual estimates of the ego-motion using a variant of the Direct Linear Transformation DLT method; the error-driven EKF exploited pseudo-measurements based on the projection errors from measured two-dimensional point features to the corresponding three-dimensional fiducials. The two filters were off-line analyzed in different experimental conditions and compared to a purely IMU-based EKF used for estimating the orientation of the IMU-camera sensor. The DLT-based EKF was more accurate than the error-driven EKF, less robust against loss of visual features, and equivalent in terms of computational complexity. Orientation root mean square errors RMSEs of 1° 1.5°, and position RMSEs of 3.5 mm 10 mm were achieved in our experiments by the DLT-based EKF error-driven EKF; by contrast, orientation RMSEs of 1.6° were achieved by the purely IMU-based EKF. View Full-Text

Keywords: sensor fusion; extended Kalman filtering; inertial-magnetic sensing; monocular vision; ego-motion sensor fusion; extended Kalman filtering; inertial-magnetic sensing; monocular vision; ego-motion





Author: Gabriele Ligorio * and Angelo Maria Sabatini

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents