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for mapping, you could use our cslibs_mapping framework. In principle, it consists of data providers, mappers and publishers and can be configured as in the exemplary launch file.
For conversion of PointClouds, you could use the cslibs_plugins_data::Pointcloud3dProvider and then the cslibs_mapping::mapper::NDTGridMapper3D or the cslibs_mapping::mapper::OccupancyNDTGridMapper3D mapper, but we currently don't have a proper visualization, the simplest would be to use the cslibs_mapping::publisher::PointcloudPublisher, which publishes the means of the joint distributions as PointCloud, but this is a rather bad representation. We hope to work on 3D (O)NDT visualization soon.
If you don't want to depend on our mapping framework, take a look at the process method in the source code of these mappers, e.g. the 3D NDT grid mapper, where the insert method of the map is called for a given PointCloud.
The matching methods are currently under development and not documented at all.
Can you provide basic usage examples about how to convert PointCloud to NDT map or perform matching?
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