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Here are the last modifications I did to improve this localization system, as I think it is not useful to let this post open forever. Note: this is not (for now) a complete solution, as there are still some points to be improved.

I adapted and tried the icp I mentionned in my question : in practice, the results are not so good. It is computationally expensive and does not really improve AMCL localization. I had some cases where the ICP gave worse results than AMCL, so I decided to not use it.

To check the AMCL output I project the lidar point cloud :

• once to the position given by AMCL
• once to the global fused position (output of global ekf node)

and check how much it matches with the map : if AMCL is the best match, position is sent to the global ekf to be fused. if global ekf is the best match, I send the current global position to amcl. If neither ekf match nor amcl match are good, we stay in "degraded" mode for a maximum distance waiting to get again a good match.

In pratice it works pretty well, excepted in open areas where a certain distance is travelled and AMCL do not manage to re-localize successfully. The difficulty here is to set a good threshold to consider when a match is "good enough" or not.

Improvement to be made

Some improvement can still be made :

• When AMCL begin to be lost its computation time to give a position increase significantly, maybe it can be used as another indicator to detect when it is lost. It is kind of ugly because setting a threshold here is dependent on the hardware and actual computation load from other nodes...

• Keep track of the count of "landmark" points in the map in a radius around the robot, and if there are too few points, stop fusing AMCL poses as there is not enough landmarks to be localized.