Automatic generation of a floor plan from a 3D scanned model: Making the Analogue World Digital

Master Thesis


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University of Cape Town

The processing of three-dimensional (3D) room models is an area of research undertaken by many academics and hobbyists due to multiple uses derived from the information obtained - such as the generation of a floor plan; an example of bridging the real and digital world. A floor plan is required when an existing room, floor, or building requires alteration. By having the floor plan in the digital domain it allows the user to alter the room via simulation and render the environment in a life-like manner to determine if the alterations will suffice. This is done using Computer Aided Design Software (CAD). Designing a new room or building would be done using CAD software. However, not all building's digital files are readily available or exist - making the creation of a floor plan necessary. The floor plan can created up by a person on pen and paper, or with using software tools and sensors. Commercial systems exist for this task but there are no automated, open-source systems that can do the same. Current research tends to focus on the processing algorithms and not the sensors or methods for capturing the environment. This dissertation deals with testing and evaluating off-the-shelf (OTS) sensors and the processing of 3D modelled rooms captured with one of these sensors. The tests performed on the OTS sensors determine the overall accuracy of the sensors for 3D room modelling. The rationale for designing and conducting these tests is to provide the community with suggested practical tests to assist in selecting an OTS sensor for 3D room modelling. The 3D room models are captured using an opensource application and are imported into custom software. The 3D models undergo pre-processing algorithms producing 2D results, which were further processed to determine the walls of rooms. The dimension information about these features are used to create a 2D floor plan. 3D modelled environments are inherently noisy, requiring efficient pre-processing to remove the noise without hampering processing performance of the 3D model. One of the largest contributors to noise and accuracy is the sensor. Selecting the appropriate sensor can mitigate the need for complex pre-processing algorithms and will improve overall processing time. The project was able to extract dimension information within an acceptable error. The tests that were designed and used for sensor testing were able to determine which sensor was the better choice for 3D room modelling. The optimal sensor was found to be Microsoft's Kinect1 . Tests were performed in which the Microsoft Kinect was required to map a room. The results show that dimensional information about the given scene could be successfully extracted with an average error of 4.60 %.