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Add paragraph 'Odometry in Global Optimization' to tuning.rst (#802) 

Follow https://github.com/googlecartographer/cartographer/pull/1029
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Michael Grupp 2018-04-11 10:28:28 +02:00 committed by Wally B. Feed
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docs/source/tuning.rst
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@ -215,3 +215,21 @@ As a next step, we strongly decrease ``global_sampling_ratio`` and ``constraint_
to compensate for the large number of constraints. to compensate for the large number of constraints.
We then tune for lower latency as explained above until the system reliably works in real time. We then tune for lower latency as explained above until the system reliably works in real time.
Odometry in Global Optimization
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If a separate odometry source is used as an input for local SLAM (``use_odometry = true``), we can also tune the global SLAM to benefit from this additional information.
There are in total four parameters that allow us to tune the individual weights of local SLAM and odometry in the optimization:
.. code-block:: lua
POSE_GRAPH.optimization_problem.local_slam_pose_translation_weight
POSE_GRAPH.optimization_problem.local_slam_pose_rotation_weight
POSE_GRAPH.optimization_problem.odometry_translation_weight
POSE_GRAPH.optimization_problem.odometry_rotation_weight
We can set these weights depending on how much we trust either local SLAM or the odometry.
By default, odometry is weighted into global optimization similar to local slam (scan matching) poses.
However, odometry from wheel encoders often has a high uncertainty in rotation.
In this case, the rotation weight can be reduced, even down to zero.