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The International Journal of Robotics Research
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Fast Ego-motion Estimation with Multi-rate Fusion of Inertial and Vision

Leopoldo Armesto

Dept. of Control Systems Engineering, Technical University of Valencia Camino de Vera, s/n 46022, Valencia, Spain, leoaran{at}isa.upv.es

Josep Tornero

Dept. of Control Systems Engineering, Technical University of Valencia Camino de Vera, s/n 46022, Valencia, Spain, jtornero{at}isa.upv.es

Markus Vincze

Automation and Control Institute Vienna University of Technology Gusshausstr. 27.29/361 A-1040, Vienna, Austria, vincze{at}acin.tuwien.ac.at

This paper presents a tracking system for ego-motion estimation which fuses vision and inertial measurements using EKF and UKF (Extended and Unscented Kalman Filters), where a comparison of their performance has been done. It also considers the multi-rate nature of the sensors: inertial sensing is sampled at a fast sampling frequency while the sampling frequency of vision is lower. the proposed approach uses a constant linear acceleration model and constant angular velocity model based on quaternions, which yields a non-linear model for states and a linear model in measurement equations. Results show that a significant improvement is obtained on the estimation when fusing both measurements with respect to just vision or just inertial measurements. It is also shown that the proposed system can estimate fast-motions even when vision system fails. Moreover, a study of the influence of the noise covariance is also performed, which aims to select their appropriate values at the tuning process. The setup is an end-effector mounted camera, which allow us to pre-define basic rotational and translational motions for validating results.

Key Words: vision and inertial • multi-rate systems • sensor fusion • tracking

The International Journal of Robotics Research, Vol. 26, No. 6, 577-589 (2007)
DOI: 10.1177/0278364907079283
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P. Gemeiner, P. Einramhof, and M. Vincze
Simultaneous Motion and Structure Estimation by Fusion of Inertial and Vision Data
The International Journal of Robotics Research, June 1, 2007; 26(6): 591 - 605.
[Abstract] [PDF]