Aerial imaging systems increasingly gain oblique viewing capabilities. Through these passive systems, photogrammetric 3D point clouds of a scene become available in addition to traditional vertical 2.5D information. In the field of urban reconstruction, this complementary information seeks for robust and automated fusion methods in order to derive 3D building geometry as well as topology in larger scales. It is sequentially shown how to get from façade planes over building footprints to roof reconstruction including overhangs. Façade planes are extracted from a photogrammetric high-resolution 3D point cloud. Local regression methods in 2D space are used to determine the local direction and a criterion for the local linearity of the point cloud. Based on these two parameters, the 3D point cloud is segmented according to which façade it belongs to. From the segmented point cloud, building footprints are extracted as polygons. Similar to cadaster information, those polygons, along with a traditional digital surface model (DSM), serve for one thing as the basis for overhang determination which is performed by fitting polynoms on the outside of façades and using their inflection points as overhang boundary. For another thing, they serve as roof areas which are segmented, topologically described and geometrically modelled. Again local regression methods are used but this time in 3D space in order to segment roof parts. Subsequently, the roof topology is derived using region growing methods. The final building models hold both, geometrical and topological properties.

We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

Keywords: Façade Splitting, Oblique True Ortho, Building Segmentation This paper introduces a method to subdivide complex building structures like terraced houses into single house units comparable to units available in a cadastral map. 3D line segments are detected with sub-pixel accuracy in traditional vertical true orthomosaics as well as in innovative oblique true orthomosaics and their respective surface models. Hereby high gradient strengths on roofs as well as fac¸ades are taken into account. By investigating the coplanarity and frequencies within a set of 3D line segments, individual cut lines for a building complex are found. The resulting regions ideally describe single houses and thus the object complexity is reduced for subsequent topological, semantical or geometrical considerations. For the chosen study area with 70 buidling outlines a hit rate of 80% for cut lines is achieved.