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How well amcl performs depends significantly on your robot setup and environment. We are using the turtlebot with a kinect as range sensor. Hereby, the kinect point cloud is projected onto the plane. In comparison to actual laser range finders, the kinect has a very limited range and amcl fails frequently in our environment. Adding custom obstacles to the map image file made things even worse for us.

On the other hand, if you have a large field of view, a high sensor range and less open-space, the custom changes in your map might be negligible for amcl compared to the amount of true positive hits.

There is a package costmap_prohibition_layer which adds custom obstacles to a new costmap layer via the ros parameter server (just define them in a yaml-file). This could be an option for you.