Particle filter
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A particle filter is a sequential Monte Carlo algorithm that estimates the state of a system by representing probability distributions as a collection of weighted random samples, called particles. Each particle encodes a hypothesis about the true state, and the filter iteratively updates particle weights based on sensor measurements and resamples to concentrate probability mass where it is most needed. In robotics and AI, particle filters are widely used for mobile robot localization, simultaneous localization and mapping (SLAM), object tracking, and sensor fusion, where system dynamics are nonlinear and noise is non-Gaussian — conditions that defeat closed-form solutions like Kalman filters. Variants such as Rao-Blackwellized particle filters combine analytical and sampled representations to handle high-dimensional problems like SLAM more efficiently, while adaptive schemes like KLD-sampling dynamically adjust particle counts to balance accuracy and computation. Particle filters matter because they provide a principled, flexible framework for probabilistic state estimation under real-world uncertainty, enabling robots to navigate, map, and track reliably even when sensors are noisy and environments are unpredictable.
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Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
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Monte Carlo localization for mobile robots
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A Tutorial on Particle Filtering and Smoothing: Fifteen years later
Randal Douc, Adam M. Johansen
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Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Kevin J. Murphy, Stuart Russell
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Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling
Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard
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State Estimation for Robotics
Timothy D. Barfoot
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Adapting the Sample Size in Particle Filters Through KLD-Sampling
Dieter Fox
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Resampling Methods for Particle Filtering: Classification, implementation, and strategies
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An efficient fastslam algorithm for generating maps of large-scale cyclic environments from raw laser range measurements
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Simultaneous Localization and Mapping
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Information Gain-based Exploration Using Rao-Blackwellized Particle Filters
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Remaining useful life prediction of lithium-ion battery with unscented particle filter technique
Qiang Miao, Lei Xie, Hengjuan Cui, Wei Liang, Michael Pecht
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Bayesian Map Learning in Dynamic Environments
Kevin P. Murphy
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Multi-robot Simultaneous Localization and Mapping using Particle Filters
Andrew Howard
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Tracking multiple moving targets with a mobile robot using particle filters and statistical data association
Dirk Schulz, Wolfram Burgard, D. Fox, Armin B. Cremers
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Particle filter-based prognostics: Review, discussion and perspectives
Marine Jouin, Rafael Gouriveau, Daniel Hissel, Marie‐Cécile Péra, Noureddine Zerhouni
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Particle Filters for Mobile Robot Localization
Dieter Fox, Sebastian Thrun, Wolfram Burgard, Frank Dellaert
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Conditional particle filters for simultaneous mobile robot localization and people-tracking
Michael Montemerlo, Sebastian Thrun, W. Whittaker
Citations: 314 • 2003
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
Austin Eliazar, Ronald Parr
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Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering
Jean-Marc Valin, François Michaud, Jean Rouat
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