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This paper presents a novel approach to the Simultaneous Localisation and Mapping (SLAM) algorithm that exploits the manner in which observations are fused into the global map of the environment to manage the computational complexity of the algorithm and improve the data association process. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in thedoi:10.1109/robot.2002.1013394 dblp:conf/icra/WilliamsDD02a fatcat:xloqyx4herfgxh6epbkhlj3dn4