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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765449
• 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.

在过去的几年里，地面车辆的移动的激光扫描（MLS），以适应大面积和高分辨率的三维数据采集的需要，有显着的发展。MLS可以快速、直接地获取三维地理空间信息，通常被识别为点云。作为城市场景分析的重要手段，从点云数据中自动提取城市场景重要结构已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巨大、类内形状变化、相邻目标之间的重叠、点密度变化、方向变化以及遮挡引起的目标不完整性。尽管对MLS点云自动处理的研究工作越来越多，但它仍然是一个开放的问题。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，图像通常是在不同的时间以不同的视角和分辨率采集的。这种情况下，需要专门的配准方法，以克服数据的异构性和在其采集配置的差异。由于城市场景中快速变化的物体数量庞大且复杂，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，以适应大规模和高分辨率3D制图的需求，我们将开发创新技术，用于提取和标记三维几何资产，并用于注册和匹配城市环境中可用的二维/三维数据集。本研究计划的目标包括：（1）增强用于超大型环境点云维数分析的体表示和基于属性的技术;（2）探索和开发用于点云直接应用和可视化的技术，特别是在基于浏览器和移动的平台引擎中; 3）开发和测试适合于异构数据匹配的基于特征的多分辨率对象识别方法; 4）设计和测试用于3D MLS数据与2D图像匹配的创新方法。我们这项研究计划的宏伟目标是开发和实施一个综合框架，为新兴的移动的技术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所需的大规模，高分辨率的室内和室外三维地图。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，而无需对城市资产进行建模。