Robust Keyframe-based Dense SLAM with an RGB-D Camera
This work addresses robust 3D mapping for VR/AR applications, though it appears incremental as it builds on existing keyframe-based SLAM methods with optimizations for efficiency.
The authors tackled the problem of robust dense SLAM with RGB-D cameras by developing RKD-SLAM, which combines color and depth information for fast keyframe-based tracking and efficient bundle adjustment. The system demonstrated effectiveness on challenging datasets including the TUM RGB-D benchmark, handling fast motion and dense loop closure without time limitations in moderate scenes.
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.