Large scale, high resolution indoor and outdoor 3d mapping with ubiquitous point clouds

在过去的几年里，地面车载移动激光扫描(MLS)技术得到了显着的发展，以适应大范围和高分辨率三维数据采集的需要。MLS可以快速、直接地获取三维地理空间信息，这些信息通常被标识为点云。作为城市场景分析的重要手段，从这些点云数据中自动提取重要的城市场景结构已经成为一个非常有吸引力的研究课题。从大规模激光扫描点云中检测目标的主要挑战是数据量大、类内形状变化、相邻目标之间重叠、点密度变化、方向变化以及遮挡导致的目标不完整。尽管人们对MLS点云自动处理的兴趣浓厚，研究工作也越来越多，但它仍然是一个悬而未决的问题。此外，城市环境中智能光学传感器的发展为MLS提供了补充的知识来源，并为提出混合现实解决方案和频繁进行变化检测提供了手段。然而，图像的采集往往是在不同的时间，以不同的视角和分辨率，而不是MLS。在这种情况下，需要专门的登记办法，以克服数据的异质性及其采购配置的差异。鉴于城市场景的复杂性和大量快速变化的目标，城市变化的自动检测被认为是一个重大的科学挑战。*通过扩展和推进我们正在进行的高效处理MLS点云的研究，以适应大规模和高分辨率3D地图的需求，我们将开发用于提取和标记3D几何资产以及注册和匹配城市环境中可用的2D/3D数据集的创新技术。研究目标包括：1)增强与超大型环境相关的点云的体积表示和基于属性的维度分析技术；2)探索和开发点云的直接应用和可视化技术，特别是在基于浏览器和移动平台的引擎中；3)开发和测试适合于异质数据匹配的基于特征的多分辨率目标识别方法；4)设计和测试一种创新的3D MLS数据与2D图像匹配的方法。我们这项研究计划的宏伟目标是开发和实施一个全面的框架，用于提供情景导航和决策支持解决方案以及沉浸式和交互式混合现实可视化的新兴移动技术所需的大规模、高分辨率室内和室外3D地图绘制。这项研究的一个关键点是，它的重点是提供直接可视化和利用点云的能力，而不需要对城市资产进行建模。

The past few years have seen remarkable development in terrestrial vehicle-based mobile laser scanning (MLS) to accommodate the need for large area and high-resolution 3d data acquisition. MLS can quickly and directly acquire 3d geospatial information, which is generally identified as point clouds. As an important approach for urban scene analysis, automatic extraction of significant urban scene structures from this point cloud data has become a highly attractive research topic. The major challenges in detecting objects from large-scale laser scanning point clouds are huge data volumes, intra-class shape variation, overlap between neighboring objects, point-density variation, orientation variation, and incompleteness of object caused by occlusion. In spite of the strong interest and increased research work on MLS point cloud automatic processing, it is still an open issue. In addition, the development of smart optical sensors in urban environment provide knowledge sources complementary to MLS as well as means to propose mixed reality solution and frequently carry out change detection. However, imagery is often collected at different time with different viewpoint and resolution than MLS. Such context requires dedicated registration approaches to overcome the heterogeneous nature of the data and the differences in their acquisition configuration. Given the complexity and vast number of rapidly changing objects in urban scene, the automatic detection of urban changes is considered a major scientific challenge.******By extending and advancing our on-going research for efficiently processing MLS point cloud to accommodate the need for large scale and high resolution 3d mapping, we will develop innovative techniques for extracting and labeling 3d geometric assets and for registering and matching 2d / 3d data sets available in urban context. The research program objectives include: 1) Enhancing volumetric representation and attribute-based techniques for dimensionality analysis of point clouds related to very large environments; 2) Exploring and developing techniques for the direct application and visualization of point clouds especially in browser based and mobile platform engine; 3) Developing and testing feature-based multi-resolution object recognition methods that are suitable for heterogeneous data matching; 4) Designing and testing an innovative approach for 3d MLS data matching with 2d imagery. Our grand goal for this research program is to develop and implement a comprehensive framework for large scale, high resolution indoor and outdoor 3d mapping required by emerging mobile technologies providing situated navigation and decision support solution as well as immersive and interactive mixed reality visualization. A pivotal point of this research is its focus on providing capabilities to directly visualize and exploit the point cloud without modeling the urban assets.