Multiswarm Particle Filter for vision based SLAM

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Multiswarm Particle Filter for vision based SLAM
Abstract— 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 kind 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.
Hee Seok Lee, Kyoung Mu Lee
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where IROS
Authors Hee Seok Lee, Kyoung Mu Lee
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