Towards a Software System for 3D Modeling of Urban Road Environments using Mobile Laser Scanning Data

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Mobile laser scanning (MLS) systems are a new and emerging technology that has demonstrated the great potential applications in critical infrastructure inventory, intelligent transportation systems, emergency response planning, and smart city development. However, how to extract geometric and semantic information directly from the 3D MLS point cloud data is a very challenging task and still remains unsolved. This research tackles the two problems. One is caused by the incompleteness, overlapping, sheltering, and similarity of the objects in the raw MLS point clouds covering the complex urban road scenes with high levels of occlusion and clutter. The other is caused by the high computational complexity due to the characteristics of large-volume, mixed-density, and spatial discreteness of raw MLS point clouds. Motivated by these challenges, this research will contribute to the MLS technology by further developing a software system that is able to handle large-volume, mixed-density MLS point clouds. The research team will develop innovative algorithms and software tools for point cloud classification, object extraction and change detection all in the 3D object space. By taking advantage of recent advances in deep learning, a special focus is given to develop algorithms for 3D point cloud classification based on deep convolutional neural networks and its implementation in a GPU (Graphics Processing Unit)-based parallel processing environment. The proposed project also undertakes development of 3D object extractors by integrating deep Boltzmann machines and Hough forests model with visibility estimation. At least two point clouds covering the same road scene that may be acquired by different MLS systems at different epochs are used to detect the geometrical changes between different epochs. The 3D change detection will be implemented through the pairwise point cloud registration based on new local feature descriptors followed by robust multi-scale tensor voting. The research results will enhance Canada's global leadership in mobile laser scanning technology, not only in its hardware but also in its software. The proposed software system will overcome the shortcomings of the current point cloud processing systems and will provide more quantitative reports of the identified changes in the urban road environment over time. The reporting system will also lead to timely and effective maintenance of the Canadian transportation infrastructures.