RGB-D dense SLAM with keyframe-based method
文献类型：会议论文
| 作者 | Fu XY(付兴银)1,2,3,4 ; Zhu F(朱枫)2,3,4; Wu QX(吴清潇)2,3,4; Sun YL(孙云雷)1,2,3,4
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| 出版日期 | 2018 |
| 会议日期 | May 22-24, 2018 |
| 会议地点 | Beijing, China |
| 关键词 | dense SLAM RGB-D camera GPU, keyframe surfel |
| 页码 | 1-11 |
| 英文摘要 | Currently, feature-based visual Simultaneous Localization and Mapping (SLAM) has reached a mature stage. Feature-based visual SLAM systems usually calculate the camera poses without producing a dense surface, even if a depth camera are provided. In contrast, dense SLAM systems simultaneously output camera poses as well as a dense surface of the reconstruction region. In this paper, we propose a new RGB-D dense SLAM system. First, camera pose is calculated by minimizing the combination of the reprojection error and the dense geometric error. We construct a new type of edge in g2o, which adds the extra constraints built with the dense geometric error to the graph optimization. The cost function is minimized in a coarse-to-fine strategy with GPU which contributes to the enhancement of system frame rate and promotion of large camera motion convergence. Second, in order to generate dense surfaces and provide users with a feedback of the scanned surfaces, we use the surfel model to fuse RGB-D streams and generated dense surface models in real-time. The surfels in the dense model are updated with embedded deformation graph to keep them consistent with the optimized camera poses after the system performs essential graph optimization and full Bundle Adjustment (BA). Third, a better 3D model is achieved by re-merging the stream with the optimized camera poses when the user ends the reconstruction. We compare the accuracy of generated camera trajectories and reconstruction surfaces with the state-of-the-art systems based on the TUM and ICL-NIUM RGB-D benchmark datasets. Experimental results show that the accuracy of dense surfaces produced online is very close to that of later re-fusion. And our system produces better results than the state-of-the-art systems in terms of the accuracy of the produced camera trajectories. © 2018 SPIE. |
| 源文献作者 | Chinese Society for Optical Engineering (CSOE) ; Division of Information and Electronic Engineering of Chinese Academy of Engineering |
| 产权排序 | 1 |
| 会议录 | Proceedings of SPIE 10845, Optical Sensing and Imaging Technologies and Applications
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| 会议录出版者 | SPIE |
| 会议录出版地 | Bellingham, USA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| ISBN号 | 978-1-5106-2333-0 |
| WOS记录号 | WOS:000455327800018 |
| 源URL | [http://ir.sia.cn/handle/173321/23950]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Fu XY(付兴银) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China 3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Fu XY,Zhu F,Wu QX,et al. RGB-D dense SLAM with keyframe-based method[C]. 见:. Beijing, China. May 22-24, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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