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Hi! I am using the ROS navigation stack with my robot. I have odometry information from the wheel encoders, and a laser range finder. I am currently using AMCL to account for odometry drift. I was wondering if it is benefitial to use an Extended Kalman Filter (EKF node) that takes in information from the wheel odometry and publishes out "odometry_filtered" which I send to AMCL and the move_base node? The odometry isn't that good with just AMCL. The robot turns left and right a lot since AMCL corrects its pose because of the odometry drift.


Originally posted by SigurdRB on ROS Answers with karma: 3 on 2017-05-20

Post score: 0

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robot_localization provides a nicely configurable Kalman Filter implementation and is probably the most frequently used package for such tasks. That being said, if you only have wheel odometry available do not expect major improvements, as KF-based estimators are most useful for fusing multiple different sources of information. A common setup would be fusing angular yaw rates from an IMU with linear velocities from wheel odometry.

If you just send wheel odometry to a EKF and nothing else. By adjusting the process and measurement noise you could add some smoothing/adjust for noise, but to see noticable improvements adding for instance an IMU to measure angular rates is recommended.


Originally posted by Stefan Kohlbrecher with karma: 24361 on 2017-05-20

This answer was ACCEPTED on the original site

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