Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration
文献类型：期刊论文
| 作者 | Huang, Kejia; Wang, Chenliang; Shi, Wenjiao |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-08-01 |
| 卷号 | 15期号:15页码:3709 |
| 关键词 | quaternion Real-Time Kinematic GPS SLAM augmented reality geo-registration sensor fusion |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs15153709 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, the accuracy and robustness of these methods are limited. To overcome these challenges, this paper proposes a rotation-invariant estimation method for high-precision geo-registration in AR maps. The method introduces several innovations. Firstly, it improves the accuracy of generating heading data from low-cost hardware by utilizing Real-Time Kinematic GPS and visual-inertial fusion. This improvement contributes to the increased stability and precise alignment of virtual objects in complex environments. Secondly, a fusion method combines the true-north direction vector and the gravity vector to eliminate alignment errors between geospatial data and the ground surface. Lastly, the proposed method dynamically combines the initial attitude relative to the geographic north direction with the motion-estimated attitude using visual-inertial fusion. This approach significantly reduces the requirements on sensor hardware quality and calibration accuracy, making it applicable to various AR precision systems such as smartphones and augmented reality glasses. The experimental results show that this method achieves AR geo-registration accuracy at the 0.1-degree level, which is about twice as high as traditional AR geo-registration methods. Additionally, it exhibits better robustness for AR applications in complex scenarios. |
| WOS关键词 | AUGMENTED REALITY ; CALIBRATION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001045672600001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194531]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Academy of Sciences 2.Institute of Geographic Sciences & Natural Resources Research, CAS |
| 推荐引用方式 GB/T 7714 | Huang, Kejia,Wang, Chenliang,Shi, Wenjiao. Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration[J]. REMOTE SENSING,2023,15(15):3709. |
| APA | Huang, Kejia,Wang, Chenliang,&Shi, Wenjiao.(2023).Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration.REMOTE SENSING,15(15),3709. |
| MLA | Huang, Kejia,et al."Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration".REMOTE SENSING 15.15(2023):3709. |
入库方式： OAI收割
来源：地理科学与资源研究所
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