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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.
From the 360° images of the former video (
• German rescue robotic center captured... ) we now generate the 3D point cloud. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The 3D point cloud generation is 5x slower than the video. It uses a VSLAM algorithm to localize the k-frames (green) and with 3 k-frames it use a 360° PatchMatch algorithm implemented at a NVIDIA graphic card (CUDA) to calculated the dense point clouds.The hall ist about 70 x 20 meters.
This video shows a model computed from 320 images taken at the Tjex 2015 of the trade project (www.tradr-project.eu). The images were acquired with a falcan 8 drone (AscTec) and reconstruct the structure with VisualSfm software. The flight was in 150 m. The Tower is about 95 meter high.
This video shows a model computed from 124 images taken at the Tjex 2015 of the trade project (www.tradr-project.eu). The images were acquired by walking around the object and reconstruct the structure with VisualSfm software.
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.
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.
360° UAV Flight in a collapse test setup at the German Resuce Robotik Center
360° Camera at a small UAV
(2021)
360° and IR- Camera Drone Flight Test: Superimposition of two data sources for Post-Fire Inspection
(2023)
This video highlights a recent flight test carried out in our cutting-edge robotics lab, unveiling the capabilities of our meticulously crafted thermal and 360° camera drone! We've ingeniously upgraded a DJI Avata with a bespoke thermal and 360° camera system. Compact yet powerful, measuring just 18 x 18 x 17 cm, this drone is strategically engineered to effortlessly navigate and deliver crucial thermal and 360° insights concurrently in post-fire or post-explosion environments.
The integration of a specialized thermal and 360° camera system enables the simultaneous capture of both data sources during a single flight. This groundbreaking approach not only reduces inspection time by half but also facilitates the seamless superimposition of thermal and 360° videos for comprehensive analysis and interpretation.
Menschen verbringen einen großen Teil ihrer Zeit in Innenräumen. Um dafür die notwendige thermische Behaglichkeit zu gewährleisten, müssen schon bei der Nutzungsplanung die Temperaturen und Luftbewegungen im Raum vorhergesagt werden können. Bei anderen Anwendungen wiederum sind diese Größen relevant für die Prozesssicherheit (z. B. Labore, Operationssaal). Die Vorhersagen erfolgen zum Beispiel durch Strömungssimulationen oder an sogenannten Mock Up Räumen, die eine 1:1 Nachbildung des relevanten Raums darstellen. Bei größeren Räumen wie z. B. Konzertsälen steigt der Aufwand erheblich an.
Eine auf den ersten Blick vergleichsweise einfache Lösung ergibt sich durch Untersuchungen an skalierten Modellräumen. Allerdings ist hier die Ähnlichkeit zwischen Modell und Realausführung bei nicht-isothermen Strömungen nicht gegeben. Die dimensionslosen Kenngrößen Reynolds Zahl Re und Archimedes Zahl Ar sind nicht identisch, da sie mit unterschiedlichen Exponenten bei der charakteristischen Länge skalieren, so dass sie durch die Wahl eines anderen Mediums oder Anpassung der Temperaturen nicht hinreichend kompensiert werden können.
Im Labor für Klimatechnik an der Westfälischen Hochschule sollen mit Hilfe von experimentellen Modelluntersuchungen und dem Vergleich mit der Realausführung Erkenntnisse gewonnen werden, in wie weit ein Kompromiss aus Ähnlichkeit und Genauigkeit gefunden werden kann, um technisch relevante Fragestellungen am Modell zu beantworten.
Die Leitfragen dabei sind:
- Wie lassen sich Modellergebnisse auf reale Raumluftströmungen übertragen?
- Unter welchen Bedingungen ist die Ähnlichkeit zwischen Modell und Realausführung noch gegeben?
- Wie sehen Leitlinien für die praktische Anwendung der Ähnlichkeitsgesetze aus?
Ein Büroraum mit unterschiedlichen Luftführungsvarianten und thermischen Lasten stellt dabei die Realausführung dar. Das Modell ist im Maßstab 1:5 herunterskaliert.
"Heuschrecken" auf Beutejagd
(2018)