# Tag Info

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I think I understand the problem more now, after your comment about ICP. Iterative Closest Point (ICP) doesn't exactly match a point or some subset of points, or even features. ICP finds the pose that minimizes the total error. What you would need to do is to define some threshold where you would consider points to be matched or unmatched. Then, you have a ...

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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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In general you have to assign some probability distribution to your map or cells individually and update them iteratively. In the very simple case you can just apply a low-pass filter to each of your cells. Like that: $p[t] = a*p[t-1] + (1-a)*I(occupied)$ where I is an indicator function which is set to 1 if current lidar measurement shows that cell is ...

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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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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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