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A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM

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Abstract

With recent advances in real-time implementations of filters for solving the simultaneous localization and mapping (SLAM) problem in the range-sensing domain, attention has shifted to implementing SLAM solutions using vision-based sensing. This paper presents and analyses different models of the Rao-Blackwellised particle filter (RBPF) for vision-based SLAM within a comprehensive application architecture. The main contributions of our work are the introduction of a new robot motion model utilizing structure from motion (SFM) methods and a novel mixture proposal distribution that combines local and global pose estimation. In addition, we compare these under a wide variety of operating modalities, including monocular sensing and the standard odometry-based methods. We also present a detailed study of the RBPF for SLAM, addressing issues in achieving real-time, robust and numerically reliable filter behavior. Finally, we present experimental results illustrating the improved accuracy of our proposed models and the efficiency and scalability of our implementation.

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  1. Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4, USA

    Robert Sim, Pantelis Elinas & James J. Little

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  1. Robert Sim
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  2. Pantelis Elinas
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  3. James J. Little
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Correspondence to Robert Sim.

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Sim, R., Elinas, P. & Little, J.J. A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM. Int J Comput Vision 74, 303–318 (2007). https://doi.org/10.1007/s11263-006-0021-0

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  • DOI: https://doi.org/10.1007/s11263-006-0021-0

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