Filtern
Erscheinungsjahr
Dokumenttyp
- Wissenschaftlicher Artikel (237)
- Konferenzveröffentlichung (216)
- Teil eines Buches (Kapitel) (32)
- Sonstiges (31)
- Video (14)
- Buch (Monographie) (13)
- Preprint (12)
- Dissertation (4)
- Bericht (4)
- Arbeitspapier (4)
Sprache
- Englisch (572) (entfernen)
Schlagworte
- Robotik (8)
- Flugkörper (7)
- UAV (7)
- Rettungsrobotik (5)
- Dissipative Particle Dynamics (4)
- Polymer-Elektrolytmembran-Brennstoffzelle (4)
- adhesion (4)
- Bionik (3)
- Deep Learning (3)
- Erweiterte Realität <Informatik> (3)
Institut
- Westfälisches Institut für Gesundheit (115)
- Westfälisches Energieinstitut (61)
- Institut für Internetsicherheit (56)
- Informatik und Kommunikation (51)
- Elektrotechnik und angewandte Naturwissenschaften (50)
- Wirtschaft und Informationstechnik Bocholt (46)
- Institut für biologische und chemische Informatik (44)
- Maschinenbau Bocholt (37)
- Institut Arbeit und Technik (15)
- Wirtschaftsingenieurwesen (15)
- Maschinenbau und Facilities Management (13)
- Institut für Innovationsforschung und -management (11)
- Fachbereiche (9)
- Wirtschaftsrecht (9)
- Mechatronik-Institut Bocholt (2)
- Strategische Projekte (2)
- Institute (1)
360° Camera at a small UAV
(2021)
Fruits (follicles) of Hakea salicifolia and Hakea sericea (Proteaceae) are characterised by pronounced lignification and open via a ventral suture and the dorsal side. The opening along both sides is unique within the Proteaceae. Both serotinous species are obligate seeders, whose spreading benefits from bush fire events. The different tissues and the course of the vascular bundles must allow the opening mechanism. While their 2D-arrangements are known to some extent from light-microscopy images of cross-sections, this work presents their three-dimensional structures and discusses their contribution to the opening of Hakea fruits. For this purpose, 3D greyscale images, reconstructed from µCT-projection data of both fruits are segmented, assisted by a deep learning algorithm (AI algorithm). 3D renderings from these segmentations show strongly interconnected vascular bundles that build a double-dome shaped network in each valve of H. salicifolia and a dome shaped honeycomb-structure in each valve of H. sericea. However, the vascular bundles of both species show no interconnection between the two lateral valves of the fruit but leave gaps for predetermined fracture tissues on the ventral and dorsal side. The opening of the fruits after a fire or after separation from the mother plant can be explained by the anisotropic shrinkage in the two valves of the fruit.
This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart from the structural information of the point clouds, it is important to mention that no further information is required for the localization. Two examples show the performance of the overall registration.
Global registration of heterogeneous ground and aerial mapping data is a challenging task. This is especially difficult in disaster response scenarios when we have no prior information on the environment and cannot assume the regular order of man-made environments or meaningful semantic cues. In this work we extensively evaluate different approaches to globally register UGV generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud maps from vision sensors. The approaches are realizations of different selections for: a) local features: key-points or segments; b) descriptors: FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR. Additionally, we compare the results against standard approaches like applying ICP after a good prior transformation has been given. The evaluation criteria include the distance which a UGV needs to travel to successfully localize, the registration error, and the computational cost. In this context, we report our findings on effectively performing the task on two new Search and Rescue datasets. Our results have the potential to help the community take informed decisions when registering point-cloud maps from ground robots to those from aerial robots.