A Kalman filter-based algorithm for IMU-camera calibration

Faraz M. Mirzaei, Stergios I. Roumeliotis

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

32 Scopus citations

Abstract

Vision-aided Inertial Navigation Systems (V-INS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera calibration process causes biases that reduce the accuracy of the estimation process and can even lead to divergence. In this paper, we present a Kalman filter-based algorithm for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlations of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3D laser scanner) except a calibration target. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages2427-2434
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
Duration: Oct 29 2007Nov 2 2007

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Country/TerritoryUnited States
CitySan Diego, CA
Period10/29/0711/2/07

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