robot_localization global param

edit

@Tom Moore I do not think that you are correct. Visual odometry if based on keypoint will localize the robot using the loop closure as it will compare the keypoints to previously stored keypoints and its covariance will collapse, ultimately fixing the odometry drift.

I was assuming that your visual odometry was using sequential frames to estimate motion, much like scan-to-scan matching can be used with planar lasers. If you have loop closure involved, then your pose will still drift over time. You'll just get a corrective signal when the loop closure happens, and you EKF pose will jump with it. But it won't correct the previous poses in the chain, like a graph-based approach might.

@Tom Moore I think you are telling obvious things which are not relevant. In order to correct the pose of the robot due to local localization drift, feature based localization is enough and we do not need to know whether the position of the robot was incorrect before. That only matters for SLAM but not for navigation. So whenever the VO based in features corrects the pose of the robot when sees the previously saved keypoint, the TF of the robot will jump to a most probable position which is enough and I do not see how the knowledge of the previous positions matter in that case. There is a reason why HMM exists

Yes, Edward, I know. I'm just letting you know how the filter will behave when you give it an absolute pose after a long period of relative-only measurements. If you're unhappy with my answers, I will close mine and you are welcome to solicit advice from someone else.

I have the same problem. But I was wondering why need to code own node like amcl when can use robot_localization package??