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

利用无处不在的点云进行大规模、高分辨率的室内和室外 3D 测绘

• 批准号: RGPIN-2018-04046
• 负责人: Daniel, Sylvie
• 金额: $ 2.62万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712414
• 关键词: Large; scale; resolution; indoor; outdoor

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.

过去几年，地面车辆移动激光扫描 (MLS) 取得了显着发展，以满足大面积和高分辨率 3D 数据采集的需求。 MLS可以快速、直接地获取3D地理空间信息，通常将其识别为点云。作为城市场景分析的重要方法，从点云数据中自动提取重要的城市场景结构已成为一个极具吸引力的研究课题。从大规模激光扫描点云中检测物体的主要挑战是巨大的数据量、类内形状变化、相邻物体之间的重叠、点密度变化、方向变化以及遮挡导致的物体不完整性。尽管人们对 MLS 点云自动处理产生了浓厚的兴趣并加大了研究工作，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提供了与MLS互补的知识来源，以及提出混合现实解决方案和频繁进行变化检测的手段。然而，与 MLS 相比，图像通常是在不同时间以不同的视点和分辨率收集的。这种情况需要专用的注册方法来克服数据的异构性及其采集配置的差异。鉴于城市场景中物体的复杂性和大量快速变化的物体，自动检测城市变化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有效处理 MLS 点云的研究，以满足大规模和高分辨率 3D 测绘的需求，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。研究计划的目标包括： 1）增强体积表示和基于属性的技术，用于与非常大的环境相关的点云的维度分析； 2）探索和开发点云的直接应用和可视化技术，特别是基于浏览器和移动平台引擎的技术； 3）开发和测试适合异构数据匹配的基于特征的多分辨率物体识别方法； 4) 设计和测试 3d MLS 数据与 2d 图像匹配的创新方法。我们此研究计划的宏伟目标是开发和实施新兴移动技术所需的大规模、高分辨率室内和室外 3D 测绘综合框架，提供定位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。这项研究的一个关键点是其重点是提供直接可视化和利用点云的能力，而无需对城市资产进行建模。