Towards Consistent Visual-Inertial Navigation

Guoquan Huang, Michael Kaess and John J. Leonard
Conference Paper, IEEE Intl. Conf. on Robotics and Automation, ICRA, June, 2014

View Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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.

To appear 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},
year = {2014},
month = {June},
} 2017-09-13T10:39:00-04:00