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

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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.