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5 votes

How to take prediction step in particle filter?

The prediction step generates a new set of states from the old set of states. The motion model of the system is used to make this best estimate of what we think the new state might be. The motion ...
Vishnu Prem's user avatar
4 votes

Implementation of a particle filter algorithm

"Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, Dieter Fox is the book you are looking for.
Octopuscabbage's user avatar
4 votes

How does fast slam creates grid maps?

Grid based FastSlam relies on the same principle that Landmakr based FastSlam. The difference is that we are not working with each grid cell as a landmark, but the whole gridmap itself. For Grid ...
Guille Sanchez's user avatar
4 votes

Localization of a Robot to find it Coordinates according to the Known Map

Particle filter According to the OP a robot with at least a distance sensor is available and a map too. That's a nice starting point for developing a hypothesis tracker aka particle filter. At first ...
Manuel Rodriguez's user avatar
3 votes

How does a clustered particle filter work?

I am going to give a 5 minutes answer because I am still wondering how do I got to this page.... =P Assuming you know Particle Filter concept. 1 - What is "Clustered Particle Filter"? The main ...
Guilherme Franco's user avatar
2 votes

Addressing the sample impoverishment in particle filter

As said by Jacob, sample impoverishment is inherit to the Sampling-Importance-Resampling family of particle filters. An alternative solution which does require some extra effort is to switch to a ...
P. Geurts's user avatar
2 votes
Accepted

Addressing the sample impoverishment in particle filter

Your description of sample impoverishment and the way to fix it seems about right. Resampling only when the variance gets low is doing exactly what you are asking for when you say the measurements ...
Jakob's user avatar
  • 3,054
2 votes

How to track multiple robots with particle filter

This is a good question, in that it's actually two good questions. The answer to which of those two options to use, is that they are the same. Your idea of "confusion" is intuitively correct, but is ...
Josh Vander Hook's user avatar
2 votes
Accepted

Why does the low variance resampling algorithm for particle filters work?

The algorithm can be understood by taking an example (using variables used in Probabilistic Robotics and algorithm in table 4.4 in page 110 in the same book). Algorithm: (Couldn't get math mode to ...
skr's user avatar
  • 239
2 votes

Help with Probabilistic Robotics Equation 13.22 detailed derivation

The third line comes from what it is called Markov Assumption and it is Stochastic Processes stuff. Basically, it says that a distribution is not altered by the insertion and/or remotion of variables ...
Akindart's user avatar
  • 129
2 votes
Accepted

Likelihood Field Matching

Basically, you are comparing the measurements of your scan with your previously computed map. You compare all your scans (line 2) for a certain time step that are not range_max (line 3). You compute ...
Guille Sanchez's user avatar
2 votes

Include information on the environment in map-aware particle filter

Answer Create a new "probability grid" for each sensing modality and combine them after you have estimated the position of the robot in each of the many different grids. In short, you have a ...
Josh Vander Hook's user avatar
1 vote

Why does the complexity of the particle filter scales exponentially with the number of dimentions?

It's the number of particles that will grow exponentially. An array of particles, with $n$ particles in each dimension, with $d$ dimensions, will have $n^d$ particles.
r-bryan's user avatar
  • 1,271
1 vote
Accepted

Why does the complexity of the particle filter scales exponentially with the number of dimentions?

You need enough particles so that you can sample across each of those dimensions. If I have a particle filter with just X position, and my particles are spread in ...
automatom's user avatar
  • 5,572
1 vote

Doubt regarding the likelihood field in measurement model

Yes, my reading is that you're correct that they are indicating the position of the kth range result. They are taking into account with the the angle of each scan ...
Tully's user avatar
  • 25k
1 vote
Accepted

How do Particle Filters give estimates of uncertainty?

For a simple case, given a particle set, calculate the weighted sum of the particles by iterating over each particle $i$ in the set $N$: $$\mu_{x} = \Sigma_{I=1}^{N} w_{i} x_{i}$$ And then calculate ...
JJB_UT's user avatar
  • 120
1 vote

2D point cloud registration success probability

Matching point clouds can be very tricky. It is kind of a needle-in-a-haystack type of problem when you don't have an initial guess at the correspondence. As you found, if the point clouds are very ...
Ben's user avatar
  • 5,855
1 vote

Vehicle Odometry Correction Using Lidar Contour Points (Localization)

ICP requires 'reference' lidar/map points which I do not have. Can I use points at t = x as a reference to update the points at t = x + 1? Reference just refers to a reference frame. Which can be ...
edwinem's user avatar
  • 1,871
1 vote

How to take prediction step in particle filter?

Like edwinem mentioned, the motion model just describes how the object should move. Consider gravity: $$ \ddot{y} = -g \\ $$ If you wanted a motion model for position, then: $$ y = y_0 + \dot{y}t + ...
Chuck's user avatar
  • 16k
1 vote

What should be the prediction step in particle filter?

The prediction step is to pass every particle's state [for e.g (x,y, theta)] through the system model that you have written as p(xk|xk-1). You have to update the state for each particle using this ...
vanshil shah's user avatar
1 vote

How to calculate the mean of an unsymmetric distribution (Particle Filter)

The mean itself is precisely defined and there's no alternative definition for it. It simply is what it is. Instead you need a different summary statistic; in this case something like a mode might be ...
ryan0270's user avatar
  • 2,804
1 vote

How should I understand sequential importance resampling in a particle filter?

After thinking about the problem, I believe the answer lies in the noise. You translate your particle "batch" every time step, and re weight each time. If $K < thresh$, you resample, with potential ...
michael zafford's user avatar
1 vote

How does fast slam creates grid maps?

I believe using a grid map will only decrease the accuracy of you estimates. I am assuming your feature map is basically a vector containing the position of each feature as real values. If you convert ...
Ralff's user avatar
  • 345
1 vote
Accepted

Why does a Bayesian Filter require random controls?

I think it is easy to see, when you take a look at the bayesian network: Now, we eliminate all the variables not given in your equation: Based on this baesian network, you can see that $u_t$ has no ...
Steffen's user avatar
  • 221
1 vote

Laser Scanner for localization in particle filter

As already stated, you can not do what you are asking. The task you need to do is score each particle's fit to the map given the incoming lidar scan. The approach i used is: for each particle do a ...
Robert Sutton's user avatar
1 vote

Laser Scanner for localization in particle filter

The RangeBearing "sensor" is used to "find" landmarks given a map of landmarks. I looked at the source code a bit and it doesn't seem to be an especially useful or realistic "sensor." Consider: Map ...
Chuck's user avatar
  • 16k
1 vote
Accepted

How to make a particle filter evaluation function with LIDAR sensing?

Having your "real" measurement, particles' state and model of taking "virtual" measurements from particles, you can define multivariate Gaussian and exploit it in order to get your probability. ...
Long Smith's user avatar
1 vote

How to implement a particle filter when sensors can't identify landmarks?

SLAM is only needed when you are also building a map. You already have a map so the problem is localization. To be exact the problem you want to research is Monte Carlo localization or particle filter ...
edwinem's user avatar
  • 1,871
1 vote

Addressing the sample impoverishment in particle filter

I'm not familiar with particle filtering, but if applying the weights between each sensor read is causing issues, why not accumulate weights to be applied between sensor calls and apply them in bulk ...
Chuck's user avatar
  • 16k
1 vote

How to calculate probability of particle survival for particle filter?

Survival rate for the case of multinomial resampling and the case of $w \geq \frac{1}{n}$ has been covered well by the accepted answer. However, I didn't find the case of $w < \frac{1}{n}$ ...
MattSt's user avatar
  • 111

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