SELF-LOCALIZATION OF A MULTI-FISHEYE CAMERA BASED AUGMENTED REALITY SYSTEM IN TEXTURELESS 3D BUILDING MODELS
Keywords: Computer vision, fisheye camera, real-time object detection, model-based tracking, augmented reality
Abstract. Georeferenced images help planners to compare and document the progress of underground construction sites. As underground positioning can not rely on GPS/GNSS, we introduce a solely vision based localization method, that makes use of a textureless 3D CAD model of the construction site. In our analysis-by-synthesis approach, depth and normal fisheye images are rendered from presampled positions and gradient orientations are extracted to build a high dimensional synthetic feature space. Acquired camera images are then matched to those features by using a robust distance metric and fast nearest neighbor search. In this manner, initial poses can be obtained on a laptop in real-time using concurrent processing and the graphics processing unit.