Geometric analysis of reality-based indoor 3D mapping

Vision based mapping has become an important way to provide geospatial information for vision based navigation especially when satellite signals are not available. When acting as an independent source for navigation, its quality will affect that of navigation directly. However, geometry is one key component that affects the quality of vision-based mapping including reliability, separability and accuracy. Analysing the geometry provides a reference for users to design and judge the mapping strategy to meet the requirement in quality. This paper aims to explore the geometry's influence on accuracy, reliability and separability in reality based indoor 3D mapping. Firstly, an analytical analysis based on the global redundancy number is conducted. Secondly, the geometric strength between the camera and ground control points (GCPs) quantified by Dilution of Precision (DoP) is analysed under different indoor mapping scenarios. Thirdly, the relationship between two geometric components including overlapping percentage and intersection angle and quality including reliability and separability is analysed based on a simulation environment. Geometric analysis shows that three images have the ability to provide enough global redundancy for reality based 3D mapping. GCPs with a good coverage of the image and a shorter distance between the camera and the object will contribute to good geometry. Besides, mapping simulation in the indoor environment based on two selected functional models shows that the number of images is the key factor that influences Minimum Detectable Bias (MDB) and Minimum Separable Bias (MSB).