Particle filter, resampling step implementation

1. What is resampling in particle filter?
2. How does a particle filter work?
3. Is particle filter a Bayesian filter?
4. What is particle filter localization?

What is resampling in particle filter?

In the resampling step, the particles with negligible weights are replaced by new particles in the proximity of the particles with higher weights.

How does a particle filter work?

Diesel particulate filters operate by trapping soot particles from the engine exhaust, preventing them from reaching the environment.

Is particle filter a Bayesian filter?

The particle filter is a Bayesian filter. This means, estimation is performed using Bayesian theory. Bayesian inference allows for estimating a state by combining a statistical model for a measurement (likelihood) with a prior probability using Bayes' theorem.

What is particle filter localization?

Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment.