Hi,
I am curious as to the coordinate frames that my odometry data is in after using the robot_localization package ekf as a localisation estimator? I am using wheel odometry and an IMU in a continuous ekf and navsat (from a gps) (and the previous two sensors) in a discrete ekf.
ekf_cont:
frequency: 30
sensor_timeout: 0.1
two_d_mode: true
transform_time_offset: 0.0
transform_timeout: 0.0
print_diagnostics: true
debug: false
map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: odom
ekf_disc:
frequency: 30
sensor_timeout: 0.1
two_d_mode: true
transform_time_offset: 0.0
transform_timeout: 0.0
print_diagnostics: true
debug: false
map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: map
My solution needs to use this data in conjunction with move_base and gmapping. As I understand it I need to provide a transform for the discrete data. My IMU device is mounted in the exact center and my drive shaft is mounted at the exact centre (in one dimension at an equal distance from the centre in the other dimension, so the speed is measured from the centre in both dimensions).
As I understand, my continuous data has to be presented to move_base in the odometry frame and my discrete data has to be presented as a tf/tfMessage? [Clearpath Husky demo](https://roscon.ros.org/2015/presentations/robot_localization.pdf). If I understand correctly my continuous is in the odometry frame and my discrete is in the map frame but how do I produce this as a tf/tfMessage for my move_base node? Will
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