12624
2015
eng
13753-13781
10
7
article
1
2016-01-29
--
--
Evaluation of a Backpack-Mounted 3D Mobile Scanning System
Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.
Remote Sensing
10.3390/rs71013753
urn:nbn:de:bvb:20-opus-126247
Remote Sensing 2015, 7, 13753-13781; doi:10.3390/rs71013753
Helge A. Lauterbach
Dorit Borrmann
Robin Heß
Daniel Eck
Klaus Schilling
Andreas Nüchter
eng
uncontrolled
man-portable mapping
eng
uncontrolled
backpack mobile mapping
eng
uncontrolled
SLAM
eng
uncontrolled
mobile laser scanning
eng
uncontrolled
personal laser scanning
Mathematische Geografie
open_access
Institut für Informatik
Förderzeitraum 2015
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/12624/Nuechter_remotesensing-07-13753.pdf
13047
2013
eng
5871-5906
11
5
article
1
2016-03-23
--
--
Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms
Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.
Remote Sensing
10.3390/rs5115871
urn:nbn:de:bvb:20-opus-130478
Remote Sensing 2013, 5, 5871-5906; doi:10.3390/rs5115871
Jan Elseberg
Dorit Borrmann
Andreas Nüchter
eng
uncontrolled
mapping
eng
uncontrolled
calibration
eng
uncontrolled
non-rigid registration
eng
uncontrolled
mobile laser scanning
eng
uncontrolled
algorithms
Mathematische Geografie
open_access
Institut für Informatik
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/13047/113_Elseberg_Borrmann_Nuechter.pdf
17664
2018
eng
1311
5
18
article
1
2019-02-14
--
--
Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data
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.
Sensors
10.3390/s18051311
urn:nbn:de:bvb:20-opus-176642
Sensors 2018, 18(5):1311. DOI: 10.3390/s18051311
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Christian Pfitzner
Stefan May
Andreas Nüchter
eng
uncontrolled
RGB-D
eng
uncontrolled
human body weight
eng
uncontrolled
image processing
eng
uncontrolled
kinect
eng
uncontrolled
machine learning
eng
uncontrolled
perception
eng
uncontrolled
segmentation
eng
uncontrolled
sensor fusion
eng
uncontrolled
stroke
eng
uncontrolled
thermal camera
Datenverarbeitung; Informatik
Inzidenz und Prävention von Krankheiten
open_access
Institut für Informatik
Förderzeitraum 2018
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/17664/Pfitzner_Sensors.pdf
18959
2016
eng
313-326
2
21
article
1
2019-10-22
--
--
Intelligent mobile system for improving spatial design support and security inside buildings
This paper concerns the an intelligent mobile application for spatial design support and security domain. Mobility has two aspects in our research: The first one is the usage of mobile robots for 3D mapping of urban areas and for performing some specific tasks. The second mobility aspect is related with a novel Software as a Service system that allows access to robotic functionalities and data over the Ethernet, thus we demonstrate the use of the novel NVIDIA GRID technology allowing to virtualize the graphic processing unit. We introduce Complex Shape Histogram, a core component of our artificial intelligence engine, used for classifying 3D point clouds with a Support Vector Machine. We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm. Our intelligent mobile system is built on top of the Qualitative Spatio-Temporal Representation and Reasoning framework. This framework defines an ontology and a semantic model, which are used for building the intelligent mobile user interfaces. We show experiments demonstrating advantages of our approach. In addition, we test our prototypes in the field after the end-user case studies demonstrating a relevant contribution for future intelligent mobile systems that merge mobile robots with novel data centers.
Mobile Networks and Applications
10.1007/s11036-015-0654-8
urn:nbn:de:bvb:20-opus-189597
Mobile Networks and Applications (2016) 21:2, S. 313-326. https://doi.org/10.1007/s11036-015-0654-8
true
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Janusz Będkowski
Karol Majek
Piotr Majek
Paweł Musialik
Michał Pełka
Andreas Nüchter
eng
uncontrolled
Intelligent mobile system
eng
uncontrolled
3D object recognition
eng
uncontrolled
Qualitative representation and reasoning
eng
uncontrolled
3D mapping
Mathematik
open_access
Institut für Informatik
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/18959/Bedkowski_MobileNetworksandApplications_2016.pdf
21998
2020
eng
23
20
article
1
--
2020-12-03
--
Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration
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.
Sensors
1424-8220
10.3390/s20236918
urn:nbn:de:bvb:20-opus-219988
2021-01-07T18:53:25+00:00
sword
swordwue
attachment; filename=deposit.zip
143ec647442cddc7f8e5bf288c3efb45
Sensors 2020, 20(23), 6918; https://doi.org/10.3390/s20236918
true
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Shitong Du
Helge A. Lauterbach
Xuyou Li
Girum G. Demisse
Dorit Borrmann
Andreas Nüchter
eng
uncontrolled
mapping
eng
uncontrolled
continuous-time SLAM
eng
uncontrolled
deformation-based method
eng
uncontrolled
time calibration
Datenverarbeitung; Informatik
open_access
Institut für Informatik
Import
Förderzeitraum 2020
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/21998/sensors-20-06918-v2.pdf
22300
2021
eng
2
21
article
1
--
2021-01-12
--
Self-Supervised point set local descriptors for Point Cloud Registration
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.
Sensors
1424-8220
10.3390/s21020486
urn:nbn:de:bvb:20-opus-223000
Sensors 2021, 21(2), 486; https://doi.org/10.3390/s21020486
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Yijun Yuan
Dorit Borrmann
Jiawei Hou
Yuexin Ma
Andreas Nüchter
Sören Schwertfeger
eng
uncontrolled
point cloud registration
eng
uncontrolled
descriptors
eng
uncontrolled
self-supervised learning
Computerprogrammierung, Programme, Daten
open_access
Institut für Informatik
Förderzeitraum 2021
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/22300/sensors-21-00486-v2.pdf
22791
2021
eng
188
report
1
2021-02-26
--
--
DAEDALUS - Descent And Exploration in Deep Autonomy of Lava Underground Structures
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.
Open Space Innovation Platform (OSIP) Lunar Caves-System Study
1868-7466
10.25972/OPUS-22791
978-3-945459-33-1
urn:nbn:de:bvb:20-opus-227911
Jacobs University Bremen, Germany
University of Padova, Italy
INAF Padova, Italy
VIGEA, Italy
publish
CC BY-NC-ND: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell, Keine Bearbeitungen 4.0 International
Angelo Pio Rossi
Francesco Maurelli
Vikram Unnithan
Hendrik Dreger
Kedus Mathewos
Nayan Pradhan
Dan-Andrei Corbeanu
Riccardo Pozzobon
Matteo Massironi
Sabrina Ferrari
Claudia Pernechele
Lorenzo Paoletti
Emanuele Simioni
Pajola Maurizio
Tommaso Santagata
Dorit Borrmann
Andreas Nüchter
Anton Bredenbeck
Jasper Zevering
Fabian Arzberger
Camilo Andrés Reyes Mantilla
Forschungsberichte in der Robotik = Research Notes in Robotics
21
eng
uncontrolled
Lunar Caves
eng
uncontrolled
Spherical Robot
eng
uncontrolled
Lunar Exploration
eng
uncontrolled
Mapping
eng
uncontrolled
3D Laser Scanning
deu
swd
Mond
deu
swd
Daedalus-Projekt
deu
swd
Lava
Datenverarbeitung; Informatik
Geowissenschaften
open_access
Institut für Informatik
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/22791/Rossi_et_al_DAEDALUS.pdf
25223
2021
eng
1
22
article
1
--
2021-12-28
--
Dynamic point cloud compression based on projections, surface reconstruction and video compression
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.
Sensors
1424-8220
10.3390/s22010197
urn:nbn:de:bvb:20-opus-252231
2022-01-03T15:13:57+00:00
sword
swordwue
attachment; filename=deposit.zip
a85072072c0f7b2994dbbcd950404dad
Sensors (2022) 22:1, 197. https://doi.org/10.3390/s22010197
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Emil Dumic
Anamaria Bjelopera
Andreas Nüchter
eng
uncontrolled
3DTK toolkit
eng
uncontrolled
map projections
eng
uncontrolled
point cloud compression
eng
uncontrolled
point-to-point measure
eng
uncontrolled
point-to-plane measure
eng
uncontrolled
Poisson surface reconstruction
eng
uncontrolled
octree
Datenverarbeitung; Informatik
open_access
Institut für Informatik
Import
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/25223/sensors-22-00197-v2.pdf
30027
2022
eng
9
article
1
--
--
--
An approach for monitoring temperature on fruit surface by means of thermal point cloud
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.
MethodsX
2215-0161
10.1016/j.mex.2022.101712
urn:nbn:de:bvb:20-opus-300270
@articleTsoulias.2022, author = Tsoulias, Nikos and Jörissen, Sven and Nüchter, Andreas, year = 2022, title = An approach for monitoring temperature on fruit surface by means of thermal point cloud, pages = 101712, volume = 9, issn = 2215-0161, journal = MethodsX, doi = 10.1016/j.mex.2022.101712,
md5:410caaff340fa0fdcef81f0d287ae10f
2023-01-19T07:09:31+00:00
/tmp/phpUmeu6F
bibtex
63c8ecab186f13.10219644
MethodsX (2022) 9:101712. https://doi.org/10.1016/j.mex.2022.101712
862665
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Nikos Tsoulias
Sven Jörissen
Andreas Nüchter
eng
uncontrolled
point cloud
eng
uncontrolled
thermal point cloud
eng
uncontrolled
fruit temperature
eng
uncontrolled
sunburn
eng
uncontrolled
food quality
eng
uncontrolled
precision horticulture
Datenverarbeitung; Informatik
open_access
Institut für Informatik
OpenAIRE
Förderzeitraum 2022
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/30027/1-s2.0-S2215016122000930-main.pdf
31373
2023
eng
9
15
article
1
--
2023-04-26
--
Lidar pose tracking of a tumbling spacecraft using the smoothed normal distribution transform
Lidar sensors enable precise pose estimation of an uncooperative spacecraft in close range. In this context, the iterative closest point (ICP) is usually employed as a tracking method. However, when the size of the point clouds increases, the required computation time of the ICP can become a limiting factor. The normal distribution transform (NDT) is an alternative algorithm which can be more efficient than the ICP, but suffers from robustness issues. In addition, lidar sensors are also subject to motion blur effects when tracking a spacecraft tumbling with a high angular velocity, leading to a loss of precision in the relative pose estimation. This work introduces a smoothed formulation of the NDT to improve the algorithm’s robustness while maintaining its efficiency. Additionally, two strategies are investigated to mitigate the effects of motion blur. The first consists in un-distorting the point cloud, while the second is a continuous-time formulation of the NDT. Hardware-in-the-loop tests at the European Proximity Operations Simulator demonstrate the capability of the proposed methods to precisely track an uncooperative spacecraft under realistic conditions within tens of milliseconds, even when the spacecraft tumbles with a significant angular rate.
Remote Sensing
2072-4292
10.3390/rs15092286
urn:nbn:de:bvb:20-opus-313738
2023-05-05T13:13:41+00:00
sword
swordwue
attachment; filename=deposit.zip
ff773e3d74303143c13a85e0de4dbc57
Remote Sensing (2023) 15:9, 2286. https://doi.org/10.3390/rs15092286
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Léo Renaut
Heike Frei
Andreas Nüchter
eng
uncontrolled
pose tracking
eng
uncontrolled
uncooperative space rendezvous
eng
uncontrolled
lidar
eng
uncontrolled
normal distribution transform
Astronomie und zugeordnete Wissenschaften
open_access
Institut für Informatik
Import
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/31373/remotesensing-15-02286-v2.pdf