  Simultaneous Localization and Mapping (SLAM) have made the real-time dense
reconstruction possible increasing the prospects of navigation, tracking, and
augmented reality problems. Some breakthroughs have been achieved in this
regard during past few decades and more remarkable works are still going on.
This paper presents an overview of SLAM approaches that have been developed
till now. Kinect Fusion algorithm, its variants, and further developed
approaches are discussed in detailed. The algorithms and approaches are
compared for their effectiveness in tracking and mapping based on Root Mean
Square error over online available datasets.
