TY - JOUR A1 - Pfitzner, Christian A1 - May, Stefan A1 - Nüchter, Andreas T1 - Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data JF - Sensors N2 - This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients. KW - RGB-D KW - human body weight KW - image processing KW - kinect KW - machine learning KW - perception KW - segmentation KW - sensor fusion KW - stroke KW - thermal camera Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-176642 VL - 18 IS - 5 ER - TY - JOUR A1 - Du, Shitong A1 - Lauterbach, Helge A. A1 - Li, Xuyou A1 - Demisse, Girum G. A1 - Borrmann, Dorit A1 - Nüchter, Andreas T1 - Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration JF - Sensors N2 - 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. KW - mapping KW - continuous-time SLAM KW - deformation-based method KW - time calibration Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219988 SN - 1424-8220 VL - 20 IS - 23 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 - TY - JOUR A1 - Dumic, Emil A1 - Bjelopera, Anamaria A1 - Nüchter, Andreas T1 - Dynamic point cloud compression based on projections, surface reconstruction and video compression JF - Sensors N2 - In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections. KW - 3DTK toolkit KW - map projections KW - point cloud compression KW - point-to-point measure KW - point-to-plane measure KW - Poisson surface reconstruction KW - octree Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252231 SN - 1424-8220 VL - 22 IS - 1 ER - TY - JOUR A1 - Tsoulias, Nikos A1 - Jörissen, Sven A1 - Nüchter, Andreas T1 - An approach for monitoring temperature on fruit surface by means of thermal point cloud JF - MethodsX N2 - Heat and excessive solar radiation can produce abiotic stresses during apple maturation, resulting fruit quality. Therefore, the monitoring of temperature on fruit surface (FST) over the growing period can allow to identify thresholds, above of which several physiological disorders such as sunburn may occur in apple. The current approaches neglect spatial variation of FST and have reduced repeatability, resulting in unreliable predictions. In this study, LiDAR laser scanning and thermal imaging were employed to detect the temperature on fruit surface by means of 3D point cloud. A process for calibrating the two sensors based on an active board target and producing a 3D thermal point cloud was suggested. After calibration, the sensor system was utilised to scan the fruit trees, while temperature values assigned in the corresponding 3D point cloud were based on the extrinsic calibration. Whereas a fruit detection algorithm was performed to segment the FST from each apple. • The approach allows the calibration of LiDAR laser scanner with thermal camera in order to produce a 3D thermal point cloud. • The method can be applied in apple trees for segmenting FST in 3D. Whereas the approach can be utilised to predict several physiological disorders including sunburn on fruit surface. KW - point cloud KW - thermal point cloud KW - fruit temperature KW - sunburn KW - food quality KW - precision horticulture Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300270 SN - 2215-0161 VL - 9 ER -