Paper Info
Title
A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM
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.
Year
DOI
Venue
2007
10.1007/s11263-006-0021-0
International Journal of Computer Vision
Keywords
Field
DocType
vision,slam,robotics,rao-blackwellised particle filters,mixture proposal,feature matching,localization
Structure from motion,Computer vision,Computer science,Particle filter,Image processing,Odometry,Pose,Artificial intelligence,Simultaneous localization and mapping,Robotics,Scalability
Journal
Volume
Issue
ISSN
74
3
0920-5691
Citations 
PageRank 
References 
34
1.20
29
Authors
3
Name
Order
Citations
PageRank
Robert Sim1341.20
Pantelis Elinas217513.21
James J. Little32430269.59