Robotics/Algorithms

particle filter 튜토리얼

Particle filters, also known as sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation.

They are usually used to estimate Bayesian models and are the sequential ('on-line') analogue of Markov chain Monte Carlo (MCMC) batch methods and are often similar to importance sampling methods. If well-designed, particle filters can be much faster than MCMC. They are often an alternative to the Extended Kalman filter (EKF) or Unscented Kalman filter (UKF) with the advantage that, with sufficient samples, they approach the Bayesian optimal estimate, so they can be made more accurate than either the EKF or UKF. The approaches can also be combined by using a version of the Kalman filter as a proposal distribution for the particle filter.

로봇의 Navigation에서 많이 사용하는 Particle 필터의 튜토리얼.

관련 연구가 많이 수행되어 있어, 관련자료는 쉽게 구할 수 있음.

관련 자료 :
- particle filter tutorial :

- 레이져 스캐너를 이용한 particle filter :

관련 URL :
- http://www.cs.washington.edu/ai/Mobile_Robotics/mcl/
- http://en.wikipedia.org/wiki/Particle_filter
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