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R-LAIR: Riverside Lab for Artificial Intelligence Research

A Particle Filter for Monocular Vision-Aided Odometry (2011)

by Teddy N. Yap, Jr., Mingyang Li, Anastasios I. Mourikis, and Christian R. Shelton

Abstract: We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.

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Teddy N. Yap, Jr., Mingyang Li, Anastasios I. Mourikis, and Christian R. Shelton (2011). "A Particle Filter for Monocular Vision-Aided Odometry." Proceedings of the IEEE International Conference on Robotics and Automation. pdf      

Bibtex citation

@inproceedings{YapLiMouShe11,
   author = "Yap, Jr., Teddy N. and Mingyang Li and Anastasios I. Mourikis and Christian R. Shelton",
   title = "A Particle Filter for Monocular Vision-Aided Odometry",
   booktitle = "Proceedings of the {IEEE} International Conference on Robotics and Automation",
   booktitleabbr = "{ICRA}-2011",
   year = 2011,
}

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