Multiswarm Particle Filter for vision based SLAM

Hee Seok Lee, Kyoung Mu Lee
2009 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Particle Filters have been widely used as a powerful optimization tool for nonlinear, non-Gaussian dynamic models such as Simultaneous Localization and Mapping (SLAM) and visual tracking. Particle filters, however, often suffer from particle impoverishment, which is caused by a mismatch between proposal distribution and target distribution. To solve this problem, we propose a new method to improve the efficiency of particle filters by employing the Particle Swarm Optimization (PSO), which is a
more » ... ind of swarm intelligence algorithm. The PSO, especially its variant for dynamic models, is combined with the generic particle filter to get samples that are well matched with target distribution. The resulting filter is applied to a vision based SLAM system and its performance is tested. We present experimental results that demonstrate improved accuracy in localization and mapping at the same or less computational cost than the conventional particle filters.
doi:10.1109/iros.2009.5354144 dblp:conf/iros/LeeL09a fatcat:ey35djhesjbmberiovyzmgbuii