| 四元组标靶辅助下的RGB-D室内点云配准 |
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| 引用本文： | 杨海舰,徐二帅,陈文佳,许志华.四元组标靶辅助下的RGB-D室内点云配准[J].测绘通报,2020,0(12):32-36. |
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| 作者姓名： | 杨海舰 徐二帅 陈文佳 许志华 |
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| 作者单位： | 1. 中国矿业大学(北京)地球科学与测绘工程学院, 北京 100083;2. 煤炭资源与安全开采国家重点实验室, 北京 100083 |
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| 基金项目： | 国家自然科学基金（41701534）；中国矿业大学（北京）大学生创新训练项目（C201802757；C201902505）；中央高校基本科研业务费专项资金（2017QK02）；煤炭资源与安全开采国家重点实验室开放基金（SKLCRSM19KFA01） |
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| 摘 要： | 面向室内弱纹理三维重建需求，本文以RGB-D摄影测量技术获取室内点云为基础，提出了四元组标靶辅助的点云配准方法。该方法首先通过阈值筛选大曲率点，自动识别邻接点云中的辅助标靶，然后采用随机采样一致性表达方法，拟合标靶参数及其中心坐标，并根据拟合参数匹配同名标靶中心，通过刚性转换完成邻接点云粗配准。在此基础上，迭代估算邻接点云间的重叠区域，优化点云间的配准参数，从而实现点云精配准。利用Kinect相机获取两类室内场景各12站点云对本文方法进行测试，试验结果表明，配准后的多站点云间距最大均方根误差优于一个采样间隔，证明了该方法在弱纹理室内点云配准中的可靠性。
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| 关 键 词： | 室内三维重建 弱纹理 Kinect 点云配准 四元组标靶 |
| 收稿时间： | 2020-01-11 |
| 修稿时间： | 2020-04-28 |
Registration for indoor RGB-D point clouds assisted by a quad- configuration target |
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| YANG Haijian,XU Ershuai,CHEN Wenjia,XU Zhihua.Registration for indoor RGB-D point clouds assisted by a quad- configuration target[J].Bulletin of Surveying and Mapping,2020,0(12):32-36. |
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| Authors: | YANG Haijian XU Ershuai CHEN Wenjia XU Zhihua |
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| Institution: | 1. College of Geoscience and Surveying Engineering, China University of Mining&Technology, Beijing 100083, China;2. State Key Laboratory of Coal Resources and Safe Mining, Beijing 100083, China |
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| Abstract: | For 3D reconstruction of textureless indoor scenarios, we designed a quad-configuration target to assist pair-wise registration of indoor RGB-D point clouds. In detail, we first detected the quad-configuration target by filtering large curvature points with threshold. Then the target parameters and centers in adjacent point clouds were fitted by the random sample consensus (RANSAC) algorithm, matching the centers of the targets by fitting parameters, and achieving rough registration via rigid transformation with 4 points congruent sets. Next, we iteratively identified the overlapping areas between adjacent point clouds. Finally, we used the optimized overlapping area to fine-tune the rigid parameters between adjacent point clouds. In order to test the usability of our method, we used a Kinect camera to acquire 12 station point clouds for two different indoor scenes, respectively. Experimental results indicated that the root mean square error between adjacent point clouds is less than one point sampling interval, which demonstrated the robustness of the proposed method in indoor scene reconstruction with less texture. |
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| Keywords: | indoor 3D reconstruction textureless scene Kinect point cloud registration quad-configuration target |
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