Robot control
Particle filter
Definition
A particle filter represents a probability distribution over possible states with a collection of weighted samples. It updates those samples using a motion model and new observations to estimate a changing state.
Also known as: Particle filtering
Updated
Many hypotheses instead of one estimate
Each particle represents a possible state, such as a robot position and orientation. Motion prediction moves the hypotheses forward, and a sensor model assigns greater weight to hypotheses that better explain the observations. Thrun's robotics paper explains this sampling approach and its use in localization and mapping.
If several corridors look alike, a filter can retain several groups of possible locations. This is useful when a single mean and covariance would obscure the ambiguity.
Resampling concentrates the computation
Resampling gives more representation to hypotheses with higher weight and removes some unlikely ones. This makes finite computing resources focus on states supported by measurements. It also means that a hypothesis discarded too early may be difficult to recover without an appropriate recovery strategy.
More dimensions need care
Particles do not eliminate the difficulty of high-dimensional state estimation. Thrun discusses methods that exploit problem structure to make large robotics problems tractable. Increasing the number of samples costs computation, and an inaccurate motion or observation model can still mislead the filter even when many particles are available.
Sources
Related terms
State estimation
State estimation infers quantities describing a robot or its environment from measurements and a model. A robot state may include position, orientation, velocity, and other variables that are not all directly measured.
Extended Kalman filter
An extended Kalman filter is a state estimator that applies Kalman-style prediction and correction to nonlinear models by locally linearizing them. It approximates uncertainty around the current state estimate.
Simultaneous localization and mapping
Simultaneous localization and mapping is the joint estimation of a robot's state and a map of its environment from sensor observations. It is commonly abbreviated SLAM.