  In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM
approach for an RGB-D camera that can robustly handle fast motion and dense
loop closure, and run without time limitation in a moderate size scene. It not
only can be used to scan high-quality 3D models, but also can satisfy the
demand of VR and AR applications. First, we combine color and depth information
to construct a very fast keyframe-based tracking method on a CPU, which can
work robustly in challenging cases (e.g.~fast camera motion and complex loops).
For reducing accumulation error, we also introduce a very efficient incremental
bundle adjustment (BA) algorithm, which can greatly save unnecessary
computation and perform local and global BA in a unified optimization
framework. An efficient keyframe-based depth representation and fusion method
is proposed to generate and timely update the dense 3D surface with online
correction according to the refined camera poses of keyframes through BA. The
experimental results and comparisons on a variety of challenging datasets and
TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.
