How do Kalman filters work in robotics?

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In summary, The conversation is about using a Kalman filter to track corner features in a sequence of image frames. The person asking for help is struggling to understand the math behind the filter and how to implement it. The expert explains that the Kalman filter is an optimal estimator for dynamic systems and can be used if the points can be represented in a certain form. The person also asks about the complexity of implementing the filter and if there are any existing codes available online. The expert offers some guidance on how to form the equations for the problem, but needs more information on the measurements and measurement error. The summary concludes with the person thanking the expert for their help.
  • #1
orochimaru
Hi,
can anyone enlighten me on kalman filter? the maths is overwhelming for me.
Thanks in advance!
 
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  • #2
Could you post a specific question? What part of the theory you don't understand?
 
  • #3
actually I'm trying to do tracking of a number of corner features across a sequence of image frames.
however, some of these features will be outside the frame after a certain number of frames. Also, some of the corners (which i use opencv to detect) may disappear in between the sequence of frames.

how should i use kalman filter in this case?
i deeply appreciate any help anyone can give because I'm really lost in how to implement this. i mean how should i set the kalman filter equations?
 
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  • #4
The Kalman filter is an optimal estimator for dynamic systems. Can you represent your points under the form:
[tex]x_{k+1} = x_k + v_{xk}T
y_{k+1} = y_k + v_{yk}T[/tex]
and the observations under the form:
[tex]z_{k+1} = f(x_{k+1}, y_{k+1}) + v_{k+1}[/tex]

Where [tex]x_k[/tex] and [tex]y_k[/tex] are the coordinates of the point in the instant [tex]t = kT[/tex], [tex]z_{k+1}[/tex] is a function of the coordinates [tex]x_{k+1}, y_{k+1}[/tex] and [tex]v_{k+1}[/tex] is the measurement noise?
If this is possible, you can use a Kalman filter (if the function [tex]f[/tex] is linear), or the Extended Kalman filter (if [tex]f[/tex] is nonlinear)
 
  • #5
my mobile robot may be moving in a straight line, could also rotate and also moving in an arc manner (in situation that it travels along a bend).
1. so is this a nonlinear system?
2. i do not have the Vxk if it refers to the velocity.
3. i have a 1000 of points to track. is this implementable?

btw, is there any such codes already available in the web? i haven't managed to find any on the web so far.
Thanks for the help.
 
  • #6
orochimaru said:
my mobile robot may be moving in a straight line, could also rotate and also moving in an arc manner (in situation that it travels along a bend).
1. so is this a nonlinear system?
Not necessarily. The equations of motion are linear, What is your measurement equation? Do you measure the x and y positions directly or do you measure angle and distance to some point?
2. i do not have the Vxk if it refers to the velocity.
No problem, the filter estimates the components of velocity for you.
3. i have a 1000 of points to track. is this implementable?
Yes.
btw, is there any such codes already available in the web? i haven't managed to find any on the web so far.
Each implementation of the Kalman filter depends on the dynamic and measurement equations, so I don't believe you could find an implemented filter that suits your application.
Thanks for the help.
You're welcome.
 
  • #7
the problem is i do not really know how to form these eqns. can someone give me some hints on how i should form the eqns for my problem?
 
  • #8
I can say how to form the dynamic equations. As for the measurement equation, I need to know what do you measure and what is the measurement error. It would be also useful to know if there is some process noise, meaning if you order your robot to go to position (1,2) for instance, it may be going to position (0.99,1.01), meaning a process noise of 0.01.
 

1. What is a Kalman filter?

A Kalman filter is an algorithm used to estimate the state of a dynamic system by combining noisy measurements with a mathematical model of the system. It is commonly used in engineering and science applications, such as navigation, control systems, and signal processing.

2. How does a Kalman filter work?

A Kalman filter works by predicting the state of a system using a mathematical model, and then combining that prediction with a noisy measurement of the system to produce a more accurate estimate of the state. This process is repeated continuously, with each new measurement updating the estimate and improving its accuracy.

3. What are the benefits of using a Kalman filter?

One of the main benefits of using a Kalman filter is its ability to handle noisy measurements and produce accurate estimates of the state of a system. It also allows for real-time estimation and can be adapted to different types of systems and measurements.

4. Are there any limitations to using a Kalman filter?

A Kalman filter relies on having a good mathematical model of the system being estimated, so if the model is inaccurate, the estimates may also be inaccurate. It also assumes that the measurement noise is normally distributed, which may not always be the case.

5. Can anyone understand and use a Kalman filter?

While the concept of a Kalman filter may seem complex, there are many resources and tutorials available for learning and implementing it. With some basic knowledge of mathematics and programming, anyone can understand and use a Kalman filter for their own projects.

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