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

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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 20172027, 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.