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As you can see there: http://www.ros.org/doc/api/geometry_msgs/html/msg/PoseWithCovariance.html the covariance is a float[36] a 6x6 matrix. diagonal terms are the trust you have in your sensor for each Dof. You have 6 Dof, position (x, y, z) and orientation (x, y, z) even if you can see the orientation in Quaternion. You can estimate your sensor or algorithm accuracy with experiment. If you see your data are good for 1cm in translation and 0.1 radian in rotation you can use this matrix: [0.01 0.0 0.0 0.0 0.0 0.0, 0.0 0.01 0.0 0.0 0.0 0.0, 0.0 0.0 0.01 0.0 0.0 0.0, 0.0 0.0 0.0 0.1 0.0 0.0, 0.0 0.0 0.0 0.0 0.1 0.0, 0.0 0.0 0.0 0.0 0.0 0.1]

If you have no information for one Dof you can put a huge value.

As you can see there: http://www.ros.org/doc/api/geometry_msgs/html/msg/PoseWithCovariance.html the covariance is a float[36] a 6x6 matrix. diagonal terms are the trust you have in your sensor for each Dof. You have 6 Dof, position (x, y, z) and orientation (x, y, z) even if you can see the orientation in Quaternion. You can estimate your sensor or algorithm accuracy with experiment. If you see your data are good for 1cm in translation and 0.1 radian in rotation you can use this matrix: matrix:

[0.01  0.0  0.0  0.0  0.0  0.0,
 0.0  0.01  0.0  0.0  0.0  0.0,
 0.0   0.0 0.01  0.0  0.0  0.0,
 0.0   0.0  0.0  0.1  0.0  0.0,
 0.0   0.0  0.0  0.0  0.1  0.0,
 0.0   0.0  0.0  0.0  0.0  0.1]
0.1]

If you have no information for one Dof you can put a huge value.