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2006
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Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters

9 years 3 months ago
Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters
This paper addresses the problem of simultaneous localization and mapping (SLAM) using vision-based sensing. We present and analyse an implementation of a RaoBlackwellised particle filter (RBPF) that uses stereo vision to localize a camera and 3D landmarks as the camera moves through an unknown environment. Our implementation is robust, can operate in real-time, and can operate without odometric or inertial measurements. Furthermore, our approach supports a 6-degree-of-freedom pose representation, vision-based ego-motion estimation, adaptive resampling, monocular operation, and a selection of odometry-based, observation-based, and mixture (combining local and global pose estimation) proposal distributions. This paper also examines the run-time behavior of efficiently designed RBPFs, providing an extensive empirical analysis of the memory and processing characteristics of RBPFs for vision-based SLAM. Finally, we present experimental results demonstrating the accuracy and efficiency of ...
Robert Sim, Pantelis Elinas, Matt Griffin, Alex Sh
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CRV
Authors Robert Sim, Pantelis Elinas, Matt Griffin, Alex Shyr, James J. Little
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