Abstract
Abstract. The necessity for the modelling of building interiors has encouraged researchers in recent years to focus on improving the capturing and modelling techniques for such environments. State-of-the-art indoor mobile mapping systems use a combination of laser scanners and/or cameras mounted on movable platforms and allow for capturing 3D data of buildings’ interiors. As GNSS positioning does not work inside buildings, the extensively investigated Simultaneous Localisation and Mapping (SLAM) algorithms seem to offer a suitable solution for the problem. Because of the dead-reckoning nature of SLAM approaches, their results usually suffer from registration errors. Therefore, indoor data acquisition has remained a challenge and the accuracy of the captured data has to be analysed and investigated. In this paper, we propose to use architectural constraints to partly evaluate the quality of the acquired point cloud in the absence of any ground truth model. The internal consistency of walls is utilized to check the accuracy and correctness of indoor models. In addition, we use a floor plan (if available) as an external information source to check the quality of the generated indoor model. The proposed evaluation method provides an overall impression of the reconstruction accuracy. Our results show that perpendicularity, parallelism, and thickness of walls are important cues in buildings and can be used for an internal consistency check.
Highlights
During the last years, the scope of indoor mapping applications has widened to be involved in many important applications such as mapping hazardous sites, indoor navigation and positioning, displaying virtual reality, etc
We look at wall thickness that characterises, like parallelism, the ability of the indoor mobile mapping system (IMMS) to keep a good localisation when moving from one room to another
We have developed a novel indoor mapping system that allows accurate 3D data acquisition based on a feature-based 6DOF Simultaneous Localisation and Mapping (SLAM) method
Summary
The scope of indoor mapping applications has widened to be involved in many important applications such as mapping hazardous sites, indoor navigation and positioning, displaying virtual reality, etc. Most traditional methods to map building interiors fundamentally depended on manual drawings, total stations, or terrestrial laser scanning (TLS) Those methods are no more applicable when we deal with complex indoor environments since they would require setting up the total station/laser scanner at many different positions, which is labour and time intensive. In order to map an indoor environment with complex structures, several indoor mapping systems mounted on moveable platforms (pushcart, robot, or human) have been developed (Bosse et al, 2012; Viametris, 2014; Wen et al, 2016) Among these systems, a number of them utilize RGBD cameras, such as Microsoft Kinect, Google Tango, a few use laser scanners, such as Google Cartographer, and some apply the integration of laser scanners and cameras on pushcarts, such as TIMMS (Trimble, 2014) and i-MMS (Viametris, 2014). 3D SLAM-based systems have been designed to explore the 3D space for cases like human navigation, rescue operations, and environment mapping (Mahon and Williams, 2003; Baglietto et al 2011)
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