particle filter

Overview

Based on Bayesian statistics.
Methods: Kalman filter, particle filter (Monte Carlo Localization).
Sensors measurements: wheel encoder, IMU, GPS, laser scanner, camera, etc.

Pros:

• Model sensor noise.
• Fuse multimodal sensor data.

Cons:

• Complex algorithms and models.
• Computationally expensive.

Function Approximation

Probabilistic method used for non-parametric function approximation.
An arbitrary function can be described by a set of $M$ particles at time $t$:

$X_t:=\{x_t^{(1)},x_t^{(2)},...,x_t^{(M)}\}$

Pros

• Estimates any posterior distribution (i.e. not limited to Gaussian distribution).
• Able to cope with noisy sensor data and inaccurate odometry.
• Easy to implement.

Cons:

• Large number of particles slows down localization.
• Requires large storage space.
• High computational resources.

Further Problems

Adaptive Monte Carlo Localization

AMCL ROS Package