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Grid based FastSlam relies on the same principle that Landmakr based FastSlam. The difference is that we are not working with each grid cell as a landmark, but the whole gridmap itself. For Grid based FastSlam, each particle updates its own grid-map using the data from the range sensor (Lidar, UltraSound, etc.) and its odometry. This is called "Mapping with ...


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There is no secret. OctoMap was not really designed with pose refinement in mind. You can even see one of the authors of RGBD-SLAM mention it here. If you did want to try and still use octomap with loop closure then the approach that you mention could work. You could probably make it a bit more efficient by only deleting the poses in Octomap that were ...


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Actually, evaluating maps with different scales and densities happens to be part of my research for my PhD so here goes the two ways I can think of on the top of my head: Visually: it is always the first step but not the only one ;). Map matching methods as in this paper by Sören Schwertfeger. That sounds like the idea you had first and I think it would ...


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I believe using a grid map will only decrease the accuracy of you estimates. I am assuming your feature map is basically a vector containing the position of each feature as real values. If you convert you feature to a grid map (occupancy grid), then you will be shifting every feature into a grid cell. This will result in a loss of resolution because the grid ...


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The question is a bit old but an answer might help. I think you are getting confused by thinking of mapping, localization and exploration as separate processes in the context of grid-based FastSLAM. In the most basic form of the algorithm you have the three steps you described: At every timestep : Update the poses of your particles using your motion model ...


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The referenced picture of an equation is this: and in latex it is: \begin{equation}\tag{26} p(z_t|m,c_t) = \frac{1}{\sqrt{2\pi\sigma^2}} e^{-\frac{1}{2}\left\{ c_{t,*}\log{\frac{z^2_{max}}{2\pi\sigma^2}} + \sum_{k=1}^{K_t}c_{t,k}\frac{(z_t-d_{t,k})^2}{\sigma^2} + c_{t,0}\frac{(z_t-z_{max})^2}{\sigma^2} \right\}} \end{equation} For a single ...


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First off, occupancy grid mapping is just one possible representation of a map. I think your question really applies many map representations. Here are my thoughts. Mapping without SLAM Whether or not you can build a map from your sensor measurements without fully implementing SLAM depends on a couple things: Do you have absolute sensors for localization? ...


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Let's asume that our 2D occupancy grid map is called G. Programmatically, G is a 2D array of float numbers between -1 and 1 called evidences. Now, to update the map, we need to : read the new laser mesurements calculate the distances of obstacles from the robot re-calculate the evidence values of G, and then replace the old values by the new evidence ...


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