A multi-state constraint Kalman filter for vision-aided inertial navigation

Anastasios I. Mourikis, Stergios I. Roumeliotis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

744 Scopus citations

Abstract

In this paper, we present an Extended Kalman Filter (EKF)-based algorithm for real-time vision-aided inertial navigation. The primary contribution of this work is the derivation of a measurement model that is able to express the geometric constraints that arise when a static feature is observed from multiple camera poses. This measurement model does not require including the 3D feature position in the state vector of the EKF and is optimal, up to linearization errors. The vision-aided inertial navigation algorithm we propose has computational complexity only linear in the number of features, and is capable of high-precision pose estimation in large-scale real-world environments. The performance of the algorithm is demonstrated in extensive experimental results, involving a camera/IMU system localizing within an urban area.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Robotics and Automation, ICRA'07
Pages3565-3572
Number of pages8
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Robotics and Automation, ICRA'07 - Rome, Italy
Duration: Apr 10 2007Apr 14 2007

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2007 IEEE International Conference on Robotics and Automation, ICRA'07
CountryItaly
CityRome
Period4/10/074/14/07

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