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AMCL can be used indoors as well as outdoors for localization depending on the application.
So.. the idea is to feed data from GPS, IMU and Wheel Odometer into a kalman filter (extended or unscented) to get a 3D pose estimate of the robot. You can directly use this in your move_base node or you can feed the output pose from the Kalman filter into the AMCL package which will use laser scans, laser based map and will output the (improved) robot's pose estimate. It is recommended to use amcl to improve your pose estimates from the Kalman Filter but you can get away without using AMCL if the output pose estimate from the Kalman filter is accurate. So, if you are satisfied with the output of Kalman filter, you can use navigation stack without AMCL otherwise using AMCL is not hard.

Hope it helps.