Automatic Creation and Real-time Update of Detailed 3D Maps Using LiDAR and Images

使用 LiDAR 和图像自动创建和实时更新详细的 3D 地图

• 批准号: RGPIN-2019-04391
• 负责人: Wang, Ruisheng
• 金额: $ 1.89万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737600
• 关键词: Automatic; Creation; Real; time; Update

Detailed and up-to-date 3D maps are essential components of applications, such as smart cities and intelligent transportation systems. In recent years, the demand for such 3D maps has dramatically increased. These needs arise particularly from self-driving cars and autonomous logistics. According to Research and Markets, the global autonomous vehicles market revenue is expected to grow at a CAGR (compound annual growth rate) of 39.6% during the forecast period 2017-2027, reaching $126.8 billion by 2027. Detailed and revised 3D maps are a prerequisite for autonomous applications, and this proposal addresses the fundamental issues associated with automated mapping. The proposed three projects exist under a unified framework that integrates multimodal data for the automatic creation of detailed 3D maps. Due to the great success of deep learning in object recognition and classification, the recognition and classification of objects from images has been largely achieved. However, this is not true for 3D point clouds, which suffer from a lack of effective 3D representation. The first project aims to develop a 3D deep learning system that uses different data structures to represent point clouds for the semantic segmentation of 3D point clouds. The second project involves the development of a satellite imagery parsing system that uses digital surface model (DSM) information. 3D maps can be generated using LiDAR, images, or a combination thereof. The combination of both constitutes a sensor fusion approach that uses the advantages of each to overcome their limitations. Given high-resolution satellite images and the corresponding DSMs, which can be regarded as 3D point clouds, the system simultaneously segments and labels images with high precision. The last project involves fine-grained façade modeling using mobile LiDAR and point clouds from aerial images. Despite detailed 3D building models being one of the key components in 3D maps, they have not been sufficiently researched. We aim to develop a fine-grained façade modeling system that is applicable for various styles. The proposed research builds upon my previous work and will significantly drive cutting-edge research in the targeted areas. The results will have a substantial economic impact on the mapping and navigation industry, location-based service providers, as well as social benefits to Canada as a whole.

详细的和最新的3D地图是应用程序的重要组成部分，例如智能城市和智能系统。和市场，在2017 - 2027年的预测期间，全球自动驾驶的收入将增长6％，到2027年达到1268亿美元。详细和修订的3D地图是与自动映射相关的自动屁股的先决条件在对象识别和分类中的自动创建详细的地图的多型数据中，从图像中识别和分类已经很大程度上了旨在开发3D深度学习系统，该系统使用不同的数据结构来重新点云，以进行3D点云的语义分割。克服其限制的两个优点的浴室含量的组合。云层的云是3D地图中的关键组成部分之一，我们旨在开发出适用于我以前的各种风格的精细粒度的立面建模系统。工作并将大大推动目标领域的尖端研究。