Browsing by Author "Khosravani, Ali Mohammad"

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Open Access
Automatic modeling of building interiors using low-cost sensor systems
(2016) Khosravani, Ali Mohammad; Fritsch, Dieter (Prof. Dr.-Ing. habil.)
Indoor reconstruction or 3D modeling of indoor scenes aims at representing the 3D shape of building interiors in terms of surfaces and volumes, using photographs, 3D point clouds or hypotheses. Due to advances in the range measurement sensors technology and vision algorithms, and at the same time an increased demand for indoor models by many applications, this topic of research has gained growing attention during the last years. The automation of the reconstruction process is still a challenge, due to the complexity of the data collection in indoor scenes, as well as geometrical modeling of arbitrary room shapes, specially if the data is noisy or incomplete. Available reconstruction approaches rely on either some level of user interaction, or making assumptions regarding the scene, in order to deal with the challenges. The presented work aims at increasing the automation level of the reconstruction task, while making fewer assumptions regarding the room shapes, even from the data collected by low-cost sensor systems subject to a high level of noise or occlusions. This is realized by employing topological corrections that assure a consistent and robust reconstruction. This study presents an automatic workflow consisting of two main phases. In the first phase, range data is collected using the affordable and accessible sensor system, Microsoft Kinect. The range data is registered based on features observed in the image space or 3D object space. A new complementary approach is presented to support the registration task in some cases where these registration approaches fail, due to the existence of insufficient visual and geometrical features. The approach is based on the user’s track information derived from an indoor positioning method, as well as an available coarse floor plan. In the second phase, 3D models are derived with a high level of details from the registered point clouds. The data is processed in 2D space (by projecting the points onto the ground plane), and the results are converted back to 3D by an extrusion (room height available from the point height histogram analysis). Data processing and modeling in 2D does not only simplify the reconstruction problem, but also allows for topological analysis using the graph theory. The performance of the presented reconstruction approach is demonstrated for the data derived from different sensors having different accuracies, as well as different room shapes and sizes. Finally, the study shows that the reconstructed models can be used to refine available coarse indoor models which are for instance derived from architectural drawings or floor plans. The refinement is performed by the fusion of the detailed models of individual rooms (reconstructed in a higher level of details by the new approach) to the coarse model. The model fusion also enables the reconstruction of gaps in the detailed model using a new learning-based approach. Moreover, the refinement process enables the detection of changes or details in the original plans, missing due to generalization purposes, or later renovations in the building interiors.