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added foldable params
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README.md

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@@ -77,7 +77,8 @@ Doing that, make sure to set the fixed frame to the map coordinate system.
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RMCL itself doesn't provide any tools to visualize the maps (triangle meshes).
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If you want to see the map in RViz, use for example the `rviz_mesh_tools_plugins` of the [mesh_tools](https://github.com/naturerobots/mesh_tools).
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Once the launch file is started, the output in Terminal should look as follows:
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<details>
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<summary>Once the launch file is started, the output in Terminal should look as follows:</summary>
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```console
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Combining Unit: CPU
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Waiting for pose guess...
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```
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</details>
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At startup, MICP does a few sanity checks for the input parameters.
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Every possible mistake in configuration can then be inferred by this output.
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For example, once there is no data available on the given `PointCloud2`-Topic it will print `data: no` instead.
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Here the 3D LiDAR is a Velodyne VLP-16 with 16 scan lines.
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The horizontal number of points is reduced to 440 and might be adjusted for your own Velodyne.
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File: `config/examples/micp_velodyne_cpu.yaml`
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<details>
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<summary>File: `config/examples/micp_velodyne_cpu.yaml` </summary>
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```yaml
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# required
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backend: embree
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```
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</details>
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### Params - 2D LiDAR + Wheels - GPU
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MICP also supports to localize a robot only equipped with a 2D LiDAR in a 3D map.
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The robot has four wheels of which the highest points are located relative to `base_footprint` as listed in `origs`.
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By setting a virtual scanner to the wheel positions scanning downwards with a constant scanning range equal to the wheel diameter it is possible to pull the robot to the mesh.
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File: `config/examples/micp_sick_gpu.yaml`
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<details>
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<summary>File: `config/examples/micp_sick_gpu.yaml`</summary>
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```yaml
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# required
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weight: 1.0
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backend: optix
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```
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</details>
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# RMCL - Project

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