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Sensors 2013, 13(2), 1919-1941; doi:10.3390/s130201919

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

The Institute of BioRobotics, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
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Received: 12 December 2012 / Revised: 24 January 2013 / Accepted: 26 January 2013 / Published: 4 February 2013
(This article belongs to the Section Physical Sensors)
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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
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Ligorio, G.; Sabatini, A.M. Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation. Sensors 2013, 13, 1919-1941.

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