@phdthesis{Borrmann2018,
  author    = {Borrmann, Dorit},
  title     = {Multi-modal 3D mapping - Combining 3D point clouds with thermal and color information},
  isbn      = {978-3-945459-20-1},
  issn      = {1868-7474},
  doi       = {10.25972/OPUS-15708},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157085},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Imagine a technology that automatically creates a full 3D thermal model of an environment and detects temperature peaks in it. For better orientation in the model it is enhanced with color information. The current state of the art for analyzing temperature related issues is thermal imaging. It is relevant for energy efficiency but also for securing important infrastructure such as power supplies and temperature regulation systems. Monitoring and analysis of the data for a large building is tedious as stable conditions need to be guaranteed for several hours and detailed notes about the pose and the environment conditions for each image must be taken. For some applications repeated measurements are necessary to monitor changes over time. The analysis of the scene is only possible through expertise and experience. This thesis proposes a robotic system that creates a full 3D model of the environment with color and thermal information by combining thermal imaging with the technology of terrestrial laser scanning. The addition of a color camera facilitates the interpretation of the data and allows for other application areas. The data from all sensors collected at different positions is joined in one common reference frame using calibration and scan matching. The first part of the thesis deals with 3D point cloud processing with the emphasis on accessing point cloud data efficiently, detecting planar structures in the data and registering multiple point clouds into one common coordinate system. The second part covers the autonomous exploration and data acquisition with a mobile robot with the objective to minimize the unseen area in 3D space. Furthermore, the combination of different modalities, color images, thermal images and point cloud data through calibration is elaborated. The last part presents applications for the the collected data. Among these are methods to detect the structure of building interiors for reconstruction purposes and subsequent detection and classification of windows. A system to project the gathered thermal information back into the scene is presented as well as methods to improve the color information and to join separately acquired point clouds and photo series. A full multi-modal 3D model contains all the relevant geometric information about the recorded scene and enables an expert to fully analyze it off-site. The technology clears the path for automatically detecting points of interest thereby helping the expert to analyze the heat flow as well as localize and identify heat leaks. The concept is modular and neither limited to achieving energy efficiency nor restricted to the use in combination with a mobile platform. It also finds its application in fields such as archaeology and geology and can be extended by further sensors.},
  subject      = {Punktwolke},
  language  = {en}
}
@article{DuLauterbachLietal.2020,
  author    = {Du, Shitong and Lauterbach, Helge A. and Li, Xuyou and Demisse, Girum G. and Borrmann, Dorit and N{\"u}chter, Andreas},
  title     = {Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {23},
  issn      = {1424-8220},
  doi       = {10.3390/s20236918},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219988},
  year      = {2020},
  abstract  = {Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.},
  language  = {en}
}
@techreport{RossiMaurelliUnnithanetal.2021,
  author    = {Rossi, Angelo Pio and Maurelli, Francesco and Unnithan, Vikram and Dreger, Hendrik and Mathewos, Kedus and Pradhan, Nayan and Corbeanu, Dan-Andrei and Pozzobon, Riccardo and Massironi, Matteo and Ferrari, Sabrina and Pernechele, Claudia and Paoletti, Lorenzo and Simioni, Emanuele and Maurizio, Pajola and Santagata, Tommaso and Borrmann, Dorit and N{\"u}chter, Andreas and Bredenbeck, Anton and Zevering, Jasper and Arzberger, Fabian and Reyes Mantilla, Camilo Andr{\´e}s},
  title     = {DAEDALUS - Descent And Exploration in Deep Autonomy of Lava Underground Structures},
  isbn      = {978-3-945459-33-1},
  issn      = {1868-7466},
  doi       = {10.25972/OPUS-22791},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-227911},
  pages     = {188},
  year      = {2021},
  abstract  = {The DAEDALUS mission concept aims at exploring and characterising the entrance and initial part of Lunar lava tubes within a compact, tightly integrated spherical robotic device, with a complementary payload set and autonomous capabilities. The mission concept addresses specifically the identification and characterisation of potential resources for future ESA exploration, the local environment of the subsurface and its geologic and compositional structure. A sphere is ideally suited to protect sensors and scientific equipment in rough, uneven environments. It will house laser scanners, cameras and ancillary payloads. The sphere will be lowered into the skylight and will explore the entrance shaft, associated caverns and conduits. Lidar (light detection and ranging) systems produce 3D models with high spatial accuracy independent of lighting conditions and visible features. Hence this will be the primary exploration toolset within the sphere. The additional payload that can be accommodated in the robotic sphere consists of camera systems with panoramic lenses and scanners such as multi-wavelength or single-photon scanners. A moving mass will trigger movements. The tether for lowering the sphere will be used for data communication and powering the equipment during the descending phase. Furthermore, the connector tether-sphere will host a WIFI access point, such that data of the conduit can be transferred to the surface relay station. During the exploration phase, the robot will be disconnected from the cable, and will use wireless communication. Emergency autonomy software will ensure that in case of loss of communication, the robot will continue the nominal mission.},
  subject      = {Mond},
  language  = {en}
}