As sensor data come in, the state of a SLAM algorithm such as Cartographer evolves to stay *the current best estimate* of a robot's trajectory and surroundings.
The most accurate localization and mapping Cartographer can offer is therefore the one obtained when the algorithm finishes.
Cartographer can serialize its internal state in a ``.pbstream`` file format which is essentially a compressed protobuf file containing a snapshot of the data structures used by Cartographer internally.
To run efficiently in real-time, Cartographer throws most of its sensor data away immediately and only works with a small subset of its input, the mapping used internally (and saved in ``.pbstream`` files) is then very rough.
However, when the algorithm finishes and a best trajectory is established, it can be recombined *a posteriori* with the full sensors data to create a high resolution map.
It will have written ``b3-2016-04-05-14-14-00.bag.pbstream`` which represents the Cartographer state after it processed all data and finished all optimizations.
When running as an online node, Cartographer doesn't know when your bag (or sensor input) ends so you need to use the exposed services to explicitly finish the current trajectory and make Cartographer serialize its current state:
For the last example, if you specify ``points.ply`` in the pipeline configuration file, this will translate to ``${HOME}/Downloads/b3-2016-04-05-14-14-00.bag_points.ply``.
For example the `assets_writer_backpack_3d.lua`_ pipeline uses ``min_max_range_filter`` to remove points that are either too close or too far from the sensor.
After this, it saves "*X-Rays*" (translucent side views of the map), then recolors the ``PointsBatch``\ s depending on the sensor frame ids and writes another set of X-Rays using these new colors.
***color_points**: Colors points with a fixed color by frame_id.
***dump_num_points**: Passes through points, but keeps track of how many points it saw and output that on Flush.
***fixed_ratio_sampler**: Only let a fixed 'sampling_ratio' of points through. A 'sampling_ratio' of 1. makes this filter a no-op.
***frame_id_filter**: Filters all points with blacklisted frame_id or a non-whitelisted frame id. Note that you can either specify the whitelist or the blacklist, but not both at the same time.
***write_hybrid_grid**: Creates a hybrid grid of the points with voxels being 'voxel_size' big. 'range_data_inserter' options are used to configure the range data ray tracing through the hybrid grid.
***intensity_to_color**: Applies ('intensity' - min) / (max - min) * 255 and color the point grey with this value for each point that comes from the sensor with 'frame_id'. If 'frame_id' is empty, this applies to all points.
***min_max_range_filtering**: Filters all points that are farther away from their 'origin' as 'max_range' or closer than 'min_range'.
***voxel_filter_and_remove_moving_objects**: Voxel filters the data and only passes on points that we believe are on non-moving objects.
***write_pcd**: Streams a PCD file to disk. The header is written in 'Flush'.
***write_ply**: Streams a PLY file to disk. The header is written in 'Flush'.
***write_probability_grid**: Creates a probability grid with the specified 'resolution'. As all points are projected into the x-y plane the z component of the data is ignored. 'range_data_inserter' options are used to cofnigure the range data ray tracing through the probability grid.
***write_xray_image**: Creates X-ray cuts through the points with pixels being 'voxel_size' big.
Two ``PointsProcessor``\ s are of particular interest: ``pcd_writing`` and ``ply_writing`` can save a point cloud in a ``.pcd`` or ``.ply`` file format.
These file formats can then be used by specialized software such as `point_cloud_viewer`_ or `meshlab`_ to navigate through the high resolution map.
The typical assets writer pipeline for this outcome is composed of an IntensityToColorPointsProcessor_ giving points a non-white color, then a PlyWritingPointsProcessor_ exporting the results to a ``.ply`` point cloud.
An example of such a pipeline is in `assets_writer_backpack_2d.lua`_.
Once you have the ``.ply``, follow the README of `point_cloud_viewer`_ to generate an on-disk octree data structure which can be viewed by one of the viewers (SDL or web based) in the same repo.
