Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Computer vision,Pattern recognition,Bundle adjustment,Computer science,RGB color model,Artificial intelligence,Computation |
DocType | Volume | Citations |
Journal | abs/1711.05166 | 1 |
PageRank | References | Authors |
0.35 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haomin Liu | 1 | 62 | 4.35 |
| Chen Li | 2 | 14 | 16.11 |
| Guojun Chen | 3 | 1 | 1.03 |
| Guofeng Zhang | 4 | 561 | 41.50 |
| Michael Kaess | 5 | 1807 | 99.52 |
| Hujun Bao | 6 | 2801 | 174.65 |