Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision(2006)
摘要
This paper addresses the problem of simultaneous localization and mapping (SLAM) using vision-based sensing. We present and analyse an implementation of a Rao- Blackwellised 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 our approach.
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关键词
camera move,rao-blackwellised particle filter,extensive empirical analysis,vision-based slam,inertial measurement,blackwellised particle filter,monocular operation,adaptive resampling,processing characteristic,vision-based ego-motion estimation,real-time vision-based slam,data structures,motion estimation,computer vision,filtering,stereo vision,simultaneous localization and mapping,robustness,degree of freedom,particle filters,pose estimation
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