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How is the (P) controller not standing up to its task ? Well, just like you said - how is it not standing up to its task? What is it doing that makes you think it's not working? You said, I tried multiple values of Kp but could not succeed Nobody here knows what that means. "Could not succeed" could be a lot of problems. My guess is that you're ...

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Concatenation will work only if your positioning/localization is super precise which is seldom the case. What you want to be doing is scan registration. ICP and NDT are the two most widely used registration techniques which you can speed up by matching only the features you are extracting. In scan registration, your sensor gets a scan (from which you will ...

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The problem with defining the terms SLAM, and odometry is that various people define them differently, and sometimes even use them interchangeably. It can also change depending on what your main sensor is(camera vs LIDAR), and even what sort of feature representation you use (scan-matching vs landmark based). So for this question I am going to use the terms ...

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You should look into localization algorithms. A good source is the book Probabilistic Robotics - W. Burgard, D. Fox and S. Thrun. If you just want to compute somehow how well it agrees you could try to compute the Root Square Mean Error of the closest point of your measurement to the point in the occupied square in the grid describing the map, given an ...

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TL;DR Nope! You don't really need Kalman filtering in this context! To perform outliers rejection, there are other techniques that you can apply successfully and more easily without the bother of dealing with the Kalman filter's requirements. Kalman filtering can be demonstrated to be the optimal stochastic filter when we have: a linear model of our ...

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You are calculating the new R, but you're not using it. You just replace the new R with the line R = self.R. You are not removing the outliers, because you are ditching that result!

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Obviously this problem can get very complicated when you just consider a 2D lidar. So I'm gonna try to keep my answer simple. You will want to estimate the position of the robot (odometry) as a minimum in order to project everything into a global space, rather than the local vehicle space. You can technically do it locally, but I personally don't think it is ...

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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 different there really isn't a great way to quantify the similarity. This holds even if the two scans are similar (or even the same!) but have very different ...

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I don't know if your confusion is with applying the transform to the points or applying it to the pose. So I'll just show you both. The easiest way is to store your points and transform in the homogenous form. For 2D the transform is a matrix(3x3) that looks like T=\begin{bmatrix} cos(\theta) & -sin(\theta) & t_x\\ sin(\theta) & cos(\theta) &...

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The referenced paper appears to be talking mainly about "mobile devices" (i.e. phones), not mobile robots. I believe there are simply physical limitations to using something like LIDAR on a phone. When holding a small device in your hand and up against your head, most of the device is covered, so optical technologies won't work well. That is why ...

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