RGB-Depth SLAM Review

TL;DR

This paper reviews RGB-Depth SLAM techniques, focusing on Kinect Fusion and its variants, comparing their effectiveness in real-time tracking and mapping for applications like navigation and augmented reality.

Contribution

It provides a comprehensive overview and comparison of existing RGB-Depth SLAM methods, highlighting advancements and effectiveness in the field.

Findings

Kinect Fusion algorithms enable real-time dense 3D reconstruction.

Variants of Kinect Fusion improve tracking accuracy.

Effectiveness of SLAM approaches evaluated using RMS error on datasets.

Abstract

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.