@article{YuanBorrmannHouetal.2021,
  author    = {Yuan, Yijun and Borrmann, Dorit and Hou, Jiawei and Ma, Yuexin and N{\"u}chter, Andreas and Schwertfeger, S{\"o}ren},
  title     = {Self-Supervised point set local descriptors for Point Cloud Registration},
  series = {Sensors},
  volume    = {21},
  journal   = {Sensors},
  number    = {2},
  issn      = {1424-8220},
  doi       = {10.3390/s21020486},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-223000},
  year      = {2021},
  abstract  = {Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.},
  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}
}