Guoquan Huang, Michael Kaess, and John J. Leonard
IEEE Intl. Conf. on Robotics and Automation, ICRA, June, 2014.
|Visual-inertial navigation systems (VINS) have prevailed in various applications, in part because of the complementary sensing capabilities and decreasing costs as well as sizes. While many of the current VINS algorithms undergo inconsistent estimation, in this paper we introduce a new extended Kalman ﬁlter (EKF)-based approach towards consistent estimates. To this end, we impose both state-transition and obervability constraints in computing EKF Jacobians so that the resulting linearized system can best approximate the underlying nonlinear system. Speciﬁcally, we enforce the propagation Jacobian to obey the semigroup property, thus being an appropriate state-transition matrix. This is achieved by parametrizing the orientation error state in the global, instead of local, frame of reference, and then evaluating the Jacobian at the propagated, instead of the updated, state estimates. Moreover, the EKF linearized system ensures correct observability by projecting the most-accurate measurement Jacobian onto the observable subspace so that no spurious information is gained. The proposed algorithm is validated by both Monte-Carlo simulation and real-world experimental tests.|
Note: To appear June 2014
|Guoquan Huang, Michael Kaess, and John J. Leonard, "Towards Consistent Visual-Inertial Navigation," IEEE Intl. Conf. on Robotics and Automation, ICRA, June, 2014.|
author = "Guoquan Huang and Michael Kaess and John J. Leonard",
title = "Towards Consistent Visual-Inertial Navigation",
booktitle = "IEEE Intl. Conf. on Robotics and Automation, ICRA",
month = "June",
year = "2014",
Notes = "To appear June 2014"
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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