TY - JOUR A1 - Yuan, Yijun A1 - Borrmann, Dorit A1 - Hou, Jiawei A1 - Ma, Yuexin A1 - Nüchter, Andreas A1 - Schwertfeger, Sören T1 - Self-Supervised point set local descriptors for Point Cloud Registration JF - Sensors N2 - 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. KW - point cloud registration KW - descriptors KW - self-supervised learning Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-223000 SN - 1424-8220 VL - 21 IS - 2 ER - TY - RPRT A1 - Rossi, Angelo Pio A1 - Maurelli, Francesco A1 - Unnithan, Vikram A1 - Dreger, Hendrik A1 - Mathewos, Kedus A1 - Pradhan, Nayan A1 - Corbeanu, Dan-Andrei A1 - Pozzobon, Riccardo A1 - Massironi, Matteo A1 - Ferrari, Sabrina A1 - Pernechele, Claudia A1 - Paoletti, Lorenzo A1 - Simioni, Emanuele A1 - Maurizio, Pajola A1 - Santagata, Tommaso A1 - Borrmann, Dorit A1 - Nüchter, Andreas A1 - Bredenbeck, Anton A1 - Zevering, Jasper A1 - Arzberger, Fabian A1 - Reyes Mantilla, Camilo Andrés T1 - DAEDALUS - Descent And Exploration in Deep Autonomy of Lava Underground Structures BT - Open Space Innovation Platform (OSIP) Lunar Caves-System Study N2 - 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. T3 - Forschungsberichte in der Robotik = Research Notes in Robotics - 21 KW - Lunar Caves KW - Spherical Robot KW - Lunar Exploration KW - Mapping KW - 3D Laser Scanning KW - Mond KW - Daedalus-Projekt KW - Lava Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-227911 SN - 978-3-945459-33-1 SN - 1868-7466 ER -