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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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I get why you are confused. Looking at the definition of PointCloud2, you see that the field that holds the "actual" point cloud data is a 1-dimensional array. Now, you might think, wait: why aren't there 3 dimensions, one for X, Y, and Z? Well, this is why we have PointCloud2: so we can have a single array in memory contain all the info we need, regardless ...


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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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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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Find extrinsic between the sensors using a calibration target or any other methods. Now that you know the relative locations of the sensors, transform the points in each sensor to the first LiDAR coordinate(e.g top left LiDAR in your figure). All the points are merged into the first LiDAR coordinate now. You need to learn 1. rigid transformation of 3d ...


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I think LiDAR is common for indoor navigation. Definitely, LiDAR is the easiest and accurate solution for indoor navigation or SLAM. Many commercial robot vacuums are already in use of LiDAR for indoor navigation and mapping. Those are even cheaper and simpler than RGBD modules which is why low-cost LiDARs are hired over RGBD in mass production models. It ...


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this may be a bit late to answer, but i hope it may be of help for people learning ros in the future. First, in the URDF model, mu1 and mu2 is friction coefficient was set as 0. If the wheel has no friction coefficient, it has no friction. Hence, the wheel will not move, since it has no friction, ...


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