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Particle Filter SLAM with High Dimensional Vehicle Model

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Abstract

This work presents a particle filter method closely related to Fastslam for solving the simultaneous localization and mapping (slam) problem. Using the standard Fastslam algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work, an extra factorization of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental data from an unmanned aerial vehicle (helicopter) are presented. The proposed algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro), barometer, and vision in order to solve the slam problem.

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Authors and Affiliations

  1. Division of Automatic Control, Department of Electrical Engineering, Linköping University, 581 83, Linköping, Sweden

    David Törnqvist, Thomas B. Schön, Rickard Karlsson & Fredrik Gustafsson

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  1. David Törnqvist
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  2. Thomas B. Schön
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  3. Rickard Karlsson
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  4. Fredrik Gustafsson
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Correspondence to David Törnqvist.

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Törnqvist, D., Schön, T.B., Karlsson, R. et al. Particle Filter SLAM with High Dimensional Vehicle Model. J Intell Robot Syst 55, 249–266 (2009). https://doi.org/10.1007/s10846-008-9301-y

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  • DOI: https://doi.org/10.1007/s10846-008-9301-y

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