Swarm intelligence
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Enhancing particle swarm optimization based particle filter tracker
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robotics and Autonomous Systems
Geometric particle swarm optimization for robust visual ego-motion estimation via particle filtering
Image and Vision Computing
Cleaning robot navigation using panoramic views and particle clouds as landmarks
Robotics and Autonomous Systems
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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.