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We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, realtime 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph optimisation. This offers the descriptive and predictivedoi:10.1109/cvpr.2013.178 dblp:conf/cvpr/Salas-MorenoNSKD13 fatcat:xgdykknqgrgvzkfb5pqqr4rkk4