# Tag Info

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I totally agree that the documentation is poor and it took me quite some time to understand what is going on. I recorded a rosbag for "velodyne_points" topic using a Velodyne VLP-16. The recorded message is PointCloud2 type. Since the recorded file was really large it gave me a lot of trouble even when only trying to take a look at it(initially I had it ...

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As Ben noticed, you may want to elaborate on your question, but in general data collection in ROS is performed using tools from the rosbag package. As you'll find when you read the documentation, data (either from a simulation or the real world) can be recorded during a session, but not after it has already finished. Recorded data can be replayed or ...

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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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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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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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You are mostly correct. You can compute the global coordinates from only a few point correspondences, but the system is not linear. If we expand the equation that described the relationship between the local and global coordinates (in 2D for simplicity), we have \begin{bmatrix} x_\mathsf{global} \\ y_\mathsf{global} \end{bmatrix} = \begin{bmatrix} \cos{...

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The process you are referring to is called point cloud registration (or point matching). The goal of point cloud registration is find the spatial transformation that aligns two point clouds (i.e., sets of points). One of the most popular methods is iterative closest point (ICP), and many variants of ICP exist. Other methods exist as well such as robust point ...

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