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融合多源数据的大规模场景三维重建方法研究
高翔
Subtype博士
Thesis Advisor胡占义
2019-05-22
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Name工学博士
Degree Discipline模式识别与智能系统
Keyword基于图像的三维建模 航拍与地面图像融合 图像与激光数据融合
Abstract

受相机运动轨迹、拍摄姿态、环境光照与遮挡以及场景自身的几何结构和纹理分布情况的影响,如何提高大规模场景三维重建的完整性和效率长期以来一直是三维重建领域的一个挑战和目标。论文围绕大规模场景重建的完整性和高效性,重点研究了基于多源数据的重建方法,特别是融合航拍图像、地面图像以及激光扫描数据的方法。论文的主要工作包括以下四个方面:

1. 针对大规模建筑场景三维重建中基于地面图像建模完整度不够而基于航拍图像建模缺乏建筑立面细节的问题,提出了一种基于稠密点云的航拍与地面点云对齐的大规模建筑场景完整建模方法。该方法采用由粗到精的流程实现航拍与地面稠密点云的对齐。为提高点云对齐的精度与效率,该方法通过对地面稠密点云进行投影的方式实现航拍视角图像的合成。在点云对齐的过程中,该方法从图像选取、合成与匹配三方面进行了改进,使得合成的图像分布均匀,噪声较小,可得到更多的匹配内点。实验结果表明,该方法可有效地实现航拍与地面模型的精确、高效对齐。

2. 针对基于稠密点云投影的点云对齐方法效率较低,合成图像噪声大、有孔洞,且通过估计相似变换实现点云对齐无法处理基于图像的建模中的场景漂移等问题,提出了一种基于稀疏点云的航拍与地面点云融合方法。该方法采用基于稀疏网格诱导单应的方式合成航拍视角图像,并采用捆绑调整的方式实现航拍与地面点云融合,在一定程度上缓解了场景漂移问题。另外,该方法采用基于几何一致性检验和几何模型验证的方式对匹配外点进行过滤,实现了航拍图像与合成图像的有效匹配。实验结果表明,该方法在点云融合精度与效率方面优于其它对比方法。

3. 针对基于图像建模依赖环境因素,精度较低而基于激光数据建模灵活性低,成本高的问题,提出了一种融合图像与激光数据的精确、完整建模方法。该方法首先对场景进行图像采集并建模,基于图像建模结果,综合考虑场景结构复杂程度、纹理丰富程度以及扫描位置分布情况,自动规划激光扫描位置。之后,该方法通过激光点云投影合成图像,并与采集图像进行匹配。基于获取的图像与激光数据之间的跨数据类型特征匹配,采用由粗到细的流程,实现图像与激光数据的融合。实验结果表明,该方法能有效地实现图像与激光数据的精确融合。

4. 针对室内场景结构复杂、纹理不丰富,基于图像的建模结果不完整、不精确的问题,提出了一种融合迷你飞行器与机器人数据的室内场景建模方法。该方法采用迷你飞行器采集图像构图,用于地面机器人路径规划并辅助机器人定位。为实现地面机器人的全局定位,该方法采用基于图割的方式合成机器人视角图像并将其与地面机器人采集的图像进行匹配。最后,通过融合迷你飞行器与地面机器人图像的方式,实现室内场景的精确、完整建模。实验结果表明,该方法可实现室内场景中地面机器人的精确定位以及场景的完整建模。

Other Abstract

How to improve the scene completeness and computational efficiency has always been a key challenge and desired objective in large-scale 3D scene reconstruction community, due to various adverse factors, such as camera trajectory and view angle changes, environmental illumination condition and occlusions, as well as scene structure complexity and texture variation. This thesis focuses on how to use multi-source data, in particular, aerial images, ground images, and laser point cloud, to enhance the scene completeness and speed up the reconstruction process. The main results and contributions of the thesis are four-fold:

1. For large-scale architectural scene 3D reconstruction, the models generated from ground images are usually incomplete, while the models generated from aerial images lack fine details on the building facades. To tackle this problem, a dense point cloud based aerial and ground point cloud registration method for complete modeling is proposed, which goes in a coarse-to-fine way. In order to improve the accuracy and efficiency of point cloud registration, the proposed method synthesizes aerial-view image via ground dense point cloud projection. During point cloud registration, the proposed method makes several improvements in image selection, synthesis, and matching to generate evenly distributed synthetic images with low noise level and to obtain more point match inliers. Experimental results demonstrate that by the proposed method, accurate and efficient aerial and ground model registration could be achieved.

2. There are several drawbacks of the dense point cloud projection based point cloud registration method, for example, (1) relatively low efficiency, (2) synthetic image with high noise level and inevitable missing pixels, and (3) a similarity transformation for point cloud registration is incapable of modeling the scene drifting issue occurred in image based modeling. To deal with these issues, a sparse point cloud based aerial and ground point cloud merging method is proposed. In the proposed method, the aerial-view image is synthesized from the homographies induced by the sparse mesh, and the aerial and ground point clouds are merged via bundle adjustment, which largely reduced the scene drifting problem. In addition, the proposed method filters the point match outliers between aerial and synthetic images via geometrical consistency check and geometrical model verification. Experimental results demonstrate that the proposed method performs better in point cloud merging accuracy and efficiency compared with other methods.

3. The models reconstructed from images are usually not accurate enough due to various factors, while the models generated from laser scans are of high cost and low flexibilities. In this work, an accurate and complete architectural scene modeling method by merging image and laser scans is proposed. The proposed method captures and models the scene using images at first. Based on the model generated from images, laser scanning locations are automatically planned by considering structural complexity and textural richness of the scene, and distribution of the scanning locations. Then, synthetic images are generated by projecting laser points, which are matched with the captured ones. Based on the cross-domain point matches, images and laser scans are merged by a coarse-to-fine scheme. Experimental results show that the proposed method could give accurate merging between images and laser scans.

4. Indoor scenes usually have complicated structures but texture paucity, it is hard to produce complete and accurate reconstructions by only image based modeling methods. This paper proposes a complete indoor scene modeling method using a mini drone and a robot. The proposed method uses aerial images captured by a mini drone to construct a global map, which is used to plan the moving path for robot and served as a global reference for robot localization. In order to localize the robot globally, the proposed method synthesizes ground-view image based on graph-cuts, which are then matched with the images captured by the robot on the ground. In the end, accurate and complete indoor scene models are achieved by merging aerial and ground images. Experimental results demonstrate that the proposed method is able to accurately localize the ground robot and completely model the indoor scene.

Pages125
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23887
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
高翔. 融合多源数据的大规模场景三维重建方法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2019.
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