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3D simultaneous localization and mapping using IMU and its observability analysis

Published online by Cambridge University Press:  09 December 2010

Farhad Aghili
Farhad Aghili*
Affiliation:
Canadian Space Agency (CSA), Space Exploration, 6767 route de l'Aeroport, Quebec J3Y 8Y9, Canada
*
*Corresponding author. Email: farhad.aghili@asc-csa.gc.ca
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Summary

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This paper investigates 3-dimensional (3D) Simultaneous Localization and Mapping (SLAM) and the corresponding observability analysis by fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF). In addition to the vehicle's states and landmark positions, the self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix and the observation sensitivity matrix are derived in closed form, making it suitable for real-time implementation. Examination of the observability of the 3D SLAM system leads to the the conclusion that the system remains observable, provided that at least three known landmarks, which are not placed in a straight line, are observed.

Keywords

Robot localizationNavigationPose estimation and registrationSLAMMobile robots

Information

Type
Articles
Information
Robotica , Volume 29 , Issue 6 , October 2011 , pp. 805 - 814
Copyright
Copyright © Cambridge University Press 2010

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