  For large-scale and long-term simultaneous localization and mapping (SLAM), a
robot has to deal with unknown initial positioning caused by either the
kidnapped robot problem or multi-session mapping. This paper addresses these
problems by tying the SLAM system with a global loop closure detection
approach, which intrinsically handles these situations. However, online
processing for global loop closure detection approaches is generally influenced
by the size of the environment. The proposed graph-based SLAM system uses a
memory management approach that only consider portions of the map to satisfy
online processing requirements. The approach is tested and demonstrated using
five indoor mapping sessions of a building using a robot equipped with a laser
rangefinder and a Kinect.
