I would like to ask you about what is the difference between particle filter when we use it in Slam methods and when we use it normally to correct our sensor measurement.

I mean, is there any difference in purpose or how we use this method (PF)

for example, I use a particle filter to localize my robot. Is it the same if I run PF SLAM to localize the robot or they have other uses aspect rather then localize.

thanks in advance.

  • $\begingroup$ As I see it, the particle filter in localization is the same as for sensor tracking. However, the measurement likelihood $p(z|x, M)$ in localization depends on the map $M$, which is typically not the case with normal sensor fusion where it often just is $p(z|x)$. Still, nobody keeps you from adding a map or other auxiliary data to a "normal" PF. Was that your question? $\endgroup$
    – André
    Jun 18 at 11:24
  • $\begingroup$ thanks for your response, after some research, I found that the problem of localization and mapping was in first treated separately, which means 'find robots pose ' independent on 'create a map for my robot', but recently the researchers unify this to be one problem 'create a map and localize my robot' which is SLAM... $\endgroup$
    – ANAS.C
    Jun 19 at 12:12
  • $\begingroup$ so as you said particle filter is just a tool to correct sensor measurement, so you can use it in SLAM (you will estimate the pose and the map at the same time) or use it separately to localize you robots $\endgroup$
    – ANAS.C
    Jun 19 at 12:16

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