| Résumé : |
(auteur) Location-based services (LBS) have gained huge commercial and scientific interest in recent years, due to the ubiquitous and free availability of maps, global positioning systems, and smartphones. To date, maps and positioning solutions are mostly only available for outdoor use. However, humans spend most of their time indoors, rendering indoor LBS interesting for applications such as location-based advertisement, customer tracking and customer flow analysis. Neither of the two prerequisites for indoor LBS - a map of the user's environment and a positioning system - is currently generally available: Most positioning methods currently under scientific investigation are based either on fingerprint maps of electro-magnetic signals (e.g. WiFi) or inertial measurement units. To overcome the flaws of these methods, they are often supported by models for the human movement which in turn rely on indoor maps. Ready-made maps, on the other hand, are generally unavailable due to indoor mapping being mostly manual, expensive and tedious. The vast amount of unmapped indoor space therefore calls for the transfer of methods used by Volunteered Geographic Information (VGI) communities like OpenStreetMap to indoor mapping. These methods comprise the digitization of features of interest such as building outlines from aerial images released to the community and the use of position traces. In this thesis, approaches are illustrated which can serve to enable this transfer. On the one hand, the thesis shows how photographs of evacuation plans - which are a compulsory part of the safety equipment of publicly used buildings in many countries - can substitute for the aerial images in the indoor domain. Due to the standardised nature of such plans, the manual digitization employed by VGI mappers in the outdoor domain can be replaced by an automatic reverse-engineering pipeline. To this end, the image is pre-processed and symbols, which depict evacuation routes or emergency equipment, are detected. Subsequently, foreground objects (i.e. walls) are distinguished from the background using an adequate binarisation operation. Based on the binary image, the sought-after vector information can be extracted by skeletonisation and skeleton tracing. The model is finalised by a bridging operation of the previously detected symbols which occlude parts of walls or stairs. As the model resulting from these operations is only available in a coordinate system defined by the original image, the transformation to a world-coordinate system or, at least, the unknown scale has to be determined. To this end, the indoor model is matched to an available model of the building's external shell. By detection of stairs, an approximate floor height can be computed and the 2D model is extruded to a 3D model. On the other hand, geometric features and semantic annotations may be added to existing models using pedestrian traces recorded by an indoor positioning system. As suitable generally available and low-cost systems do not exist yet, their existence is simulated in this work by a dead-reckoning system basing on a foot-mounted inertial measurement system. Methods for the derivation of the initial position and orientation necessary for the application of such a system are shown, as well as methods enabling the correction of remaining errors. The latter comprise an alignment approach using the external building shell and a map-matching method which employs the existing coarse model derived from the evacuation plan. Building on the collected pedestrian traces, semi-automatic and automatic approaches for the existing models' semantic and geometric refinement are presented which range from semantic annotation using the analysis of photographed doorplates to automatic door reconstruction. Furthermore, a geometric update of single rooms by conjoint analysis of the coarse model, pedestrian traces and a hand-held low-cost range camera is described. Lastly, works of indoor mapping are presented which are based on pedestrian traces and higher-level knowledge about the interior structure of the building modelled in an indoor grammar. Due to the differing characteristics of the two central elements of building interiors, corridors and rooms, the grammar is composed of a Lindenmayer system modelling the floor's corridor system and a split grammar describing the room layout which is found in the non-corridor spaces. The grammar is put to the test by applying it to distributedly collected noisy trace data. |
Crowd-sourced reconstruction of building interiors (2016)
