3D Mapping and Change Detection in Indoor Environments Using Multisource LiDAR Point Clouds

使用多源 LiDAR 点云在室内环境中进行 3D 测绘和变化检测

• 批准号: RGPIN-2022-03741
• 负责人: Li, Jonathan
• 金额: $ 5.92万
• 依托单位: University of Waterloo
• 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=754888
• 关键词: 3D; Mapping; Change; Detection; Indoor

Indoor mobile mapping is important for a wide range of applications ranging from indoor navigation, hazard mapping, autonomous parking, facility management, interior design, to virtual tourism. Today, indoor mobile laser scanning (iMLS) systems with multi--line, solid-state, and area-array LiDAR sensors can acquire point clouds (sets of data points) that represent not only static objects but also 3D motion data of moving objects in the indoor or underground space. As precise positioning with a Global Navigation Satellite System (GNSS) is often not attainable inside buildings. By integrating the LiDAR sensors with a low-cost inertial measurement unit (IMU) and a camera, an iMLS system allows the capture of 3D point cloud data from building interiors. However, besides the characteristics of large volume, spatial discreteness, and point density variation over distances of iMLS point clouds, the incompleteness, occlusion, and similarity of 3D objects in complex indoor spaces also make 3D object localization, recognition and change detection considerably challenging. To date, the process of converting point clouds into attributed spatial objects is operator-driven and has not yet reached automation. To tackle these challenges, this research program aims to develop novel strategies, methods, and software tools that can rapidly and accurately update 3D indoor maps from 3D point clouds acquired by various types of low-cost in MLS systems in GNSS--denied indoor environments. This research includes four main themes: (A) to investigate robust fusion frameworks for handling multi-source iMLS point clouds, (B) to develop new quality assessment metrics and multi-scale iMLS data enhancement approaches, (C) to explore weakly supervised learning approaches to 3D object detection and change detection, and (D) to develop extensible iMLS software prototype towards an interactive segmentation operation of large-scale point clouds. It is anticipated that this research will make three potential contributions to indoor mapping: (1) a theoretical framework for the generalized 3D feature description and 3D object recognition to handle multi-source iMLS point clouds, (2) the weakly supervised learning algorithms that require fewer label data, and (3) an open-source software prototype for indoor mapping and change detection. The developed algorithms and software prototype will overcome the shortcomings of current point cloud processing tools for indoor mapping and provide more quantitative reports of the identified changes in indoor environments. The research results will enhance Canada's world leadership in mobile LiDAR mapping technology and geospatial mapping industry.

室内移动地图对于从室内导航、危险地图、自动停车、设施管理、室内设计到虚拟旅游等广泛的应用都很重要。如今，室内移动激光扫描（iMLS）系统具有多线、固态和区域阵列激光雷达传感器，可以获取点云（数据点集），这些点云不仅代表静态物体，还代表室内或地下空间中运动物体的3D运动数据。由于全球导航卫星系统（GNSS）的精确定位通常无法在建筑物内实现。通过将激光雷达传感器与低成本惯性测量单元（IMU）和摄像头集成，iMLS系统可以从建筑物内部捕获3D点云数据。然而，iMLS点云除了具有体积大、空间离散、点密度随距离变化等特点外，复杂室内空间中三维物体的不完整性、遮挡和相似性也给三维物体的定位、识别和变化检测带来了很大的挑战。迄今为止，将点云转换为属性空间对象的过程是由操作员驱动的，尚未达到自动化。为了应对这些挑战，本研究计划旨在开发新的策略、方法和软件工具，以便在GNSS拒绝的室内环境中，从各种低成本MLS系统获得的3D点云中快速准确地更新3D室内地图。本研究包括四个主要主题：(A)研究用于处理多源iMLS点云的鲁棒融合框架；(B)开发新的质量评估指标和多尺度iMLS数据增强方法；(C)探索用于3D目标检测和变化检测的弱监督学习方法；(D)开发用于大规模点云交互式分割操作的可扩展iMLS软件原型。预计本研究将对室内制图做出三个潜在贡献：(1)为处理多源iMLS点云的广义3D特征描述和3D物体识别提供理论框架；(2)需要较少标签数据的弱监督学习算法；(3)为室内制图和变化检测提供开源软件原型。所开发的算法和软件原型将克服当前用于室内测绘的点云处理工具的缺点，并为室内环境中识别的变化提供更多的定量报告。该研究成果将增强加拿大在移动激光雷达测绘技术和地理空间测绘行业的世界领先地位。