RGB-Depth SLAM Review
This is an incremental review paper summarizing existing SLAM methods for researchers and practitioners in navigation, tracking, and augmented reality.
This paper provides a comprehensive review of RGB-Depth SLAM approaches, comparing algorithms like Kinect Fusion and its variants for their effectiveness in tracking and mapping using Root Mean Square error on available datasets.
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.