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Integrative Computational Design and Construction for Architecture (T3.0)
Research
Research Projects
RP 26-2 – Collaborative Human-Robot Teams for On-Site Construction

Collaborative Human-Robot Teams for On-Site Construction

Research Project 26-2 (RP 26-2)

COLLABORATIVE TEAMS OF MOBILE MANIPULATORS WITH ADVANCED CAPABILITIES AND HUMANS FOR ON-SITE CONSTRUCTION AND RENOVATION

The project will study how heterogeneous mobile manipulator teams collaborate with human workers during on-site renovation and construction of existing buildings, aiming to create an integrated human-machine co-agency framework for safe, efficient, meaningful collaboration in complex settings. Combining social science, design, and engineering, it will embed robots into current workflows and organizations. Guided by empirical studies and stakeholder input, it will define co-agency scenarios linking human skill and judgment with robotic autonomy and precision. Technically, it will develop highly adaptive, autonomous robots that will perceive, coordinate, and purposefully improvise, with Mixed-Reality interfaces enabling workers to interpret and collaborate effectively with these systems. Validation will focus on ecologically valid renovation use cases, notably the assembly and disassembly of timber stud wall systems, which comprise representative tasks transferable across a broad range of interior construction and renovation applications. Depending on project opportunities, the frameworks may be further examined in serial facade renovation. These realistic renovation scenarios will demonstrate gains in productivity, precision, and sustainability. The results will yield socio-technical design principles and integration guidelines for human-centered, flexible, and sustainable robotic construction.

PRINCIPAL INVESTIGATORS

Prof. Dr. Kai Arras
Principal Investigator
Institute for Artificial Intelligence (KI)
Cluster of Excellence IntCDC

Prof. Dr. Cordula Kropp
Board of Directors
Institute for Social Sciences (SOWI)
Cluster of Excellence IntCDC

Prof. Achim Menges
Spokesperson
Institute for Computational Design and Construction (ICD)
Cluster of Excellence IntCDC

Prof. Dr.-Ing. habil. Dr. h.c. Oliver Sawodny
Board of Directors
Institute for System Dynamics (ISYS)
Cluster of Excellence IntCDC

PARTICIPATING RESEARCHERS

Prof. Dr. Michael Sedlmair
Participating Researcher
Visualization Research Center (VISUS)
Cluster of Excellence IntCDC

RESEARCHERS

M.Sc. Kaan Aytekin
Doctoral Researcher
Institute for Artificial Intelligence (KI)
Cluster of Excellence IntCDC

M.A. Amelie Schreck
Doctoral Researcher
Institute for Social Sciences (SOWI)
Cluster of Excellence IntCDC

B.Sc. Lena Kerber
Maternity/paternity leave cover
Institute for Social Sciences (SOWI)
Cluster of Excellence IntCDC

M.Sc. Xiliu Yang
Postdoctoral Researcher
Institute for Computational Design and Construction (ICD)
Cluster of Excellence IntCDC

M.Sc. Samuel Slezak
Doctoral Researcher
Institute for Computational Design and Construction (ICD)
Cluster of Excellence IntCDC

M.Sc. Alice Hierholz
Doctoral Researcher
Institute for System Dynamics (ISYS)
Cluster of Excellence IntCDC

M.Sc. Jonas Vogelsang
Doctoral Researcher
Visualization Research Center (VISUS)
Cluster of Excellence IntCDC

Dipl.-Ing. Jan Krieglstein
Doctoral Researcher
Visualization Research Center (VISUS)
Cluster of Excellence IntCDC

PEER-REVIEWED PUBLICATIONS

2026
1. Abolhasani, S., Balangé, L., Zhang, L., Reß, V., Haala, N., Elshani, D., Wortmann, T., Sörgel, U., & Schwieger, V. (2026). Capturing and Modeling of Existing Buildings from Point Clouds Under the Application of Stochastic Information. In A. Kopácik, P. Kyrinovic, J. Erdélyi, & J. Bures (Eds.), Contributions to International Conferences on Engineering Surveying (pp. 111–124). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-12070-0_10
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  @inproceedings{10.1007/978-3-032-12070-0_10, abstract = {Extending the existing building stocks is a principal task to meet the enormous demand for inner-city living, minimize the need for additional transport infrastructure, and reduce environmental pollution and land consumption. The Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC) at the University of Stuttgart focuses on this topic through a co-design approach for integrating computational design, engineering, and robotic construction. To enable construction in existing buildings, a reliable representation of the as-built geometry of the building is needed. However, this data is often not available, but the design plans are available, which may not always match the current state of the building, or there may have been deviations already during construction. This paper brings forward a novel approach for the geometry representation, taking advantage of different geometry sources and their stochastic models, including floor plans, Terrestrial Laser Scanning (TLS) and Simultaneous Localization and Mapping (SLAM). Initially, the as-planned geometry was modeled digitally based on the floor plans and considering the stochastic information. In this contribution, the Building and Habitats object Model (BHoM) was utilized instead of data schemas like Industry Foundation Classes (IFC) since it fulfills the specific co-designing requirements. Secondly, the generated TLS and SLAM point clouds were processed, and stochastic models were attached. Finally, to generate a comprehensive geometry representation, the segmented geometric primitives from different sources were fused based on the respective approximated stochastic models. The results were integrated into the BHoM for expansion purposes.}, address = {Cham}, author = {Abolhasani, Sahar and Balangé, Laura and Zhang, Li and Re{\ss}, Vincent and Haala, Norbert and Elshani, Diellza and Wortmann, Thomas and S{\"o}rgel, Uwe and Schwieger, Volker}, booktitle = {Contributions to International Conferences on Engineering Surveying}, doi = {10.1007/978-3-032-12070-0_10}, editor = {Kop{\'a}{\v{c}}ik, Alojz and Kyrinovi{\v{c}}, Peter and Erd{\'e}lyi, J{\'a}n and Bure{\v{s}}, Ji{\v{r}}{\'i}}, isbn = {978-3-032-12070-0}, pages = {111--124}, publisher = {Springer Nature Switzerland}, title = {Capturing and Modeling of Existing Buildings from Point Clouds Under the Application of Stochastic Information}, year = 2026 }
2. Hierholz, A., Reß, V., Hentschel, N., Gienger, A., Haala, N., & Sawodny, O. (2026). Active visual SLAM using potential fields and model predictive control. Control Engineering Practice, 172, 106897. https://doi.org/10.1016/j.conengprac.2026.106897
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  @article{Hierholz_2026, author = {Hierholz, Alice and Reß, Vincent and Hentschel, Nils and Gienger, Andreas and Haala, Norbert and Sawodny, Oliver}, doi = {10.1016/j.conengprac.2026.106897}, issn = {0967-0661}, journal = {Control Engineering Practice}, month = {07}, pages = 106897, publisher = {Elsevier BV}, title = {Active visual SLAM using potential fields and model predictive control}, url = {http://dx.doi.org/10.1016/j.conengprac.2026.106897}, volume = 172, year = 2026 }
3. Hierholz, A., Röhm, E., Gienger, A., & Sawodny, O. (2026). Distributed Trajectory Generation based on ADMM for Collaborative Workpiece Transport with Mobile Robots. IEEE 19th International Conference on Advanced Motion Control (AMC). https://doi.org/10.1109/AMC67705.2026.11435816
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  @inproceedings{hierholz2026distributed, author = {Hierholz, Alice and Röhm, Emil and Gienger, Andreas and Sawodny, Oliver}, booktitle = {IEEE 19th International Conference on Advanced Motion Control (AMC)}, doi = {https://doi.org/10.1109/AMC67705.2026.11435816}, title = {Distributed Trajectory Generation based on ADMM for Collaborative Workpiece Transport with Mobile Robots}, year = 2026 }
4. Kropp, C., & Renner, T. (2026). Automation, Co-Agency, and Distributed Responsibility: Caring for Hybrid Therapeutic Networks. Analyse & Kritik, 48, Article 1. https://doi.org/10.1515/auk-2026-3005
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  @article{Kropp_2026, abstract = {Drawing on insights from sociology and science and technology studies, we develop an account of agency as an emergent and relational property constituted within hybrid configurations of humans and machines. We speak of co-agency to capture the ways in which responsible action is jointly produced within human- machine entanglements, with both parties demonstrating purposively intended as well as determined forms of action. By emphasizing the interdependence of sociotechnical systems, therapeutic practices, and institutional automation infrastructures, we argue that responsibility cannot be attributed to discrete actors – whether human or digital – but must instead be understood as generated within the relational processes through which co-agency emerges. This reconceptualization shifts attention from locating isolated bearers of responsibility to examining the socio-technical arrangements that structure action, thereby suggesting recommendations for ethical guidelines aimed at supporting responsible automation in healthcare.}, author = {Kropp, Cordula and Renner, Tobias}, doi = {10.1515/auk-2026-3005}, issn = {2365-9858}, journal = {Analyse & Kritik}, language = {engl}, month = {05}, number = 1, pages = {49–70}, publisher = {Walter de Gruyter GmbH}, title = {Automation, Co-Agency, and Distributed Responsibility: Caring for Hybrid Therapeutic Networks}, url = {http://dx.doi.org/10.1515/auk-2026-3005}, volume = 48, year = 2026 }
5. Wehmeier, M., Hain, S., Hagedorn, M., Pfeifer, D., & Sawodny, O. (2026). A Modeling Approach for Robotic Handling of Deformable Objects in Food Retail to Enable Virtual Commissioning. IEEE 19th International Conference on Advanced Motion Control (AMC).
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  @inproceedings{wehmeier2026modeling, author = {Wehmeier, Marc and Hain, Sören and Hagedorn, Marcel and Pfeifer, Denis and Sawodny, Oliver}, booktitle = {IEEE 19th International Conference on Advanced Motion Control (AMC)}, title = {A Modeling Approach for Robotic Handling of Deformable Objects in Food Retail to Enable Virtual Commissioning}, year = 2026 }
2025
1. Friz, F., Hierholz, A., Sawodny, O., & Uchiyama, N. (2025). Function-Parameterized Optimal Trajectory Generation for Mobile Robots. IEEE/IFAC 9th International Conference on Control Automation and Diagnosis (ICCAD′25).
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  @inproceedings{friz2025functionparameterized, author = {Friz, Fabian and Hierholz, Alice and Sawodny, Oliver and Uchiyama, Naoki}, booktitle = {IEEE/IFAC 9th International Conference on Control Automation and Diagnosis (ICCAD'25)}, title = {Function-Parameterized Optimal Trajectory Generation for Mobile Robots}, year = 2025 }
2. Ress, V., Meyer, J., Zhang, W., Skuddis, D., Soergel, U., & Haala, N. (2025). 3D Gaussian Splatting aided Localization for Large and Complex Indoor-Environments. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-G-2025, 1283–1290. https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1283-2025
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  @article{isprs-archives-XLVIII-G-2025-1283-2025, author = {Ress, V. and Meyer, J. and Zhang, W. and Skuddis, D. and Soergel, U. and Haala, N.}, doi = {10.5194/isprs-archives-XLVIII-G-2025-1283-2025}, journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {1283--1290}, title = {3D Gaussian Splatting aided Localization for Large and Complex Indoor-Environments}, url = {https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1283/2025/}, volume = {XLVIII-G-2025}, year = 2025 }
3. Zhang, W., Cheng, Q., Skuddis, D., Zeller, N., Cremers, D., & Haala, N. (2025). HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction. IEEE Transactions on Robotics, 41, 6478–6493. https://doi.org/10.1109/TRO.2025.3626627
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  @article{11219329, abstract = {We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing neural SLAM or 3DGS-based SLAM methods often tradeoff between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneously with RGB input alone. The key idea of our approach is to enhance the ability for geometry estimation by combining easy-to-obtain monocular priors with learning-based dense SLAM, and then using 3-D Gaussian splatting as our core map representation to efficiently model the scene. Upon loop closure, our method ensures on-the-fly global consistency through efficient pose graph bundle adjustment and instant map updates by explicitly deforming the 3-D Gaussian units based on anchored keyframe updates. Furthermore, we introduce a grid-based scale alignment strategy to maintain improved scale consistency in prior depths for finer depth details. Through extensive experiments on Replica, ScanNet, Waymo Open, ETH3D SLAM and ScanNet++ datasets, we demonstrate significant improvements over existing neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality.}, author = {Zhang, Wei and Cheng, Qing and Skuddis, David and Zeller, Niclas and Cremers, Daniel and Haala, Norbert}, doi = {10.1109/TRO.2025.3626627}, journal = {IEEE Transactions on Robotics}, pages = {6478-6493}, title = {HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction}, volume = 41, year = 2025 }
4. Zhang, X., Lin, D., & Soergel, U. (2025). Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 220, 32–50. https://doi.org/10.1016/j.isprsjprs.2024.11.012
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  @article{ZHANG202532, abstract = {Semantic interpretation of 3D scenes poses a formidable challenge in point cloud processing, which also stands as a requisite undertaking across various fields of application involving point clouds. Although a number of point cloud segmentation methods have achieved leading performance, 3D rare class segmentation continues to be a challenge owing to the imbalanced distribution of fine-grained classes and the complexity of large scenes. In this paper, we present target-aware attentional network (TaaNet), a novel mask-constrained attention framework to address 3D semantic segmentation of imbalanced classes in large-scale point clouds. Adapting the self-attention mechanism, a hierarchical aggregation strategy is first applied to enhance the learning of point-wise features across various scales, which leverages both global and local perspectives to guarantee presence of fine-grained patterns in the case of scenes with high complexity. Subsequently, rare target masks are imposed by a contextual module on the hierarchical features. Specifically, a target-aware aggregator is proposed to boost discriminative features of rare classes, which constrains hierarchical features with learnable adaptive weights and simultaneously embeds confidence constraints of rare classes. Furthermore, a target pseudo-labeling strategy based on strong contour cues of rare classes is designed, which effectively delivers instance-level supervisory signals restricted to rare targets only. We conducted thorough experiments on four multi-platform LiDAR benchmarks, i.e., airborne, mobile and terrestrial platforms, to assess the performance of our framework. Results demonstrate that compared to other commonly used advanced segmentation methods, our method can obtain not only high segmentation accuracy but also remarkable F1-scores in rare classes. In a submission to the official ranking page of Hessigheim 3D benchmark, our approach achieves a state-of-the-art mean F1-score of 83.84% and an outstanding overall accuracy (OA) of 90.45%. In particular, the F1-scores of rare classes namely vehicles and chimneys notably exceed the average of other published methods by a wide margin, boosting by 32.00% and 32.46%, respectively. Additionally, extensive experimental analysis on benchmarks collected from multiple platforms, Paris-Lille-3D, Semantic3D and WHU-Urban3D, validates the robustness and effectiveness of the proposed method.}, author = {Zhang, Xinlong and Lin, Dong and Soergel, Uwe}, doi = {https://doi.org/10.1016/j.isprsjprs.2024.11.012}, issn = {0924-2716}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, pages = {32-50}, title = {Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds}, url = {https://www.sciencedirect.com/science/article/pii/S0924271624004222}, volume = 220, year = 2025 }
2024
1. Collmar, D., Walter, V., & Soergel, U. (2024). Crowd Controls Crowd: Quality Improvement of Polygon Integration in Paid Crowdsourcing. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4-2024, 83–90. https://doi.org/10.5194/isprs-annals-X-4-2024-83-2024
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  @article{isprs-annals-X-4-2024-83-2024, author = {Collmar, D. and Walter, V. and Soergel, U.}, doi = {10.5194/isprs-annals-X-4-2024-83-2024}, journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {83--90}, title = {Crowd Controls Crowd: Quality Improvement of Polygon Integration in Paid Crowdsourcing}, url = {https://isprs-annals.copernicus.org/articles/X-4-2024/83/2024/}, volume = {X-4-2024}, year = 2024 }
2. Gienger, A., Stein, C., Lauer, A. P. R., Sawodny, O., & Tarín, C. (2024). Data-Based Reachability Analysis and Optimized Robot Positioning for Co-Design of Construction Processes. 2024 IEEE/SICE International Symposium on System Integration (SII).
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  @conference{noauthororeditor, author = {Gienger, Andreas and Stein, Charlotte and Lauer, Anja Patricia Regina and Sawodny, Oliver and Tarín, Cristina}, booktitle = {2024 IEEE/SICE International Symposium on System Integration (SII)}, title = {Data-Based Reachability Analysis and Optimized Robot Positioning for Co-Design of Construction Processes}, year = 2024 }
3. Hierholz, A., Gienger, A., & Sawodny, O. (2024). Improving Data-based Trajectory Generation by Quadratic Programming for Redundant Mobile Manipulators*. Proceedings of the 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 771–776. https://doi.org/10.1109/AIM55361.2024.10636991
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  @inproceedings{10636991, abstract = {Challenges in trajectory generation in robotics such as nonlinearities, non-convex constraints, many optimization variables and redundancy lead to new developments in the use of data-based methods. In this paper, a combination of a data- and model-based approach for trajectory generation for a redundant mobile manipulator with 10 degrees of freedom is presented with the goal of reducing the computational cost and improving scalability. A computationally efficient neural network regression model is proposed which predicts the joint trajectories generated from an optimal control problem considering the system equations, redundancy, singularities, nonlinear kinematic and dynamic constraints as well as collisions. The prediction is then improved through a subsequent quadratic programming with low computational cost, ensuring the compliance with the system equations and increasing the tool-center-point target reaching accuracy.}, author = {Hierholz, Alice and Gienger, Andreas and Sawodny, Oliver}, booktitle = {Proceedings of the 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)}, doi = {10.1109/AIM55361.2024.10636991}, issn = {2159-6255}, month = {07}, pages = {771-776}, title = {Improving Data-based Trajectory Generation by Quadratic Programming for Redundant Mobile Manipulators*}, year = 2024 }
4. Ress, V., Brändle, M., & Haala, N. (2024). Analysis and Implementation of Rotation-invariant Neural Network Architectures for Feature Extraction. 44. Wissenschaftlich-Technische Jahrestagung Der DGPF in Remagen – Publikationen Der DGPF, 32, 12–19. https://www.dgpf.de/src/tagung/jt2024/proceedings/paper/02_KKNP_dgpf2024_Ress_et_al.pdf
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  @article{ress2024analysis, author = {Ress, V. and Brändle, M. and Haala, N.}, journal = {44. Wissenschaftlich-Technische Jahrestagung der DGPF in Remagen – Publikationen der DGPF}, pages = {12-19}, title = {Analysis and Implementation of Rotation-invariant Neural Network Architectures for Feature Extraction }, url = {https://www.dgpf.de/src/tagung/jt2024/proceedings/paper/02_KKNP_dgpf2024_Ress_et_al.pdf }, volume = 32, year = 2024 }
5. Ress, V., Zhang, W., Skuddis, D., Haala, N., & Soergel, U. (2024). SLAM for Indoor Mapping of Wide Area Construction Environments. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-2-2024, 209–216. https://doi.org/10.5194/isprs-annals-X-2-2024-209-2024
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  @article{isprs-annals-X-2-2024-209-2024, abstract = {Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring.}, author = {Ress, V. and Zhang, W. and Skuddis, D. and Haala, N. and Soergel, U.}, doi = {10.5194/isprs-annals-X-2-2024-209-2024}, journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {209--216}, title = {SLAM for Indoor Mapping of Wide Area Construction Environments}, url = {https://isprs-annals.copernicus.org/articles/X-2-2024/209/2024/}, volume = {X-2-2024}, year = 2024 }
2023
1. Collmar, D., Walter, V., Koelle, M., & Soergel, U. (2023). FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, 159–166. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-159-2023
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  @article{isprs-annals-X-1-W1-2023-159-2023, author = {Collmar, D. and Walter, V. and Koelle, M. and Soergel, U.}, doi = {10.5194/isprs-annals-X-1-W1-2023-159-2023}, journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {159--166}, title = {FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA}, url = {https://isprs-annals.copernicus.org/articles/X-1-W1-2023/159/2023/}, volume = {X-1/W1-2023}, year = 2023 }
2. Collmar, D., Walter, V., Koelle, M., & Soergel, U. (2023). A two-step approach for the acquisition of individual tree outlines using paid crowdsourcing. 43. Wissenschaftlich-Technische Jahrestagung Der DGPF, 22.-23. März 2023 in München, 163–173. https://doi.org/10.24407/KXP:1841079707
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  @inproceedings{TIBKAT:1841079707, address = {Stuttgart}, author = {Collmar, David and Walter, Volker and Koelle, Michael and Soergel, Uwe}, booktitle = {43. Wissenschaftlich-Technische Jahrestagung der DGPF, 22.-23. März 2023 in München}, doi = {10.24407/KXP:1841079707}, pages = {163-173}, publisher = {Geschäftsstelle der DGPF }, title = {A two-step approach for the acquisition of individual tree outlines using paid crowdsourcing}, url = {https://www.tib.eu/de/suchen/id/TIBKAT%3A1841079707}, year = 2023 }
3. Haala, N., Zhang, W., Joachim, L., & Skuddis, D. (2023). SLAM für die Echtzeit-Erfassung von Innenräumen. 22. Internationale Geodätische Woche Obergurgl 2022, 188–179.
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  @article{haala2023echtzeiterfassung, author = {Haala, N. and Zhang, W. and Joachim, L. and Skuddis, D.}, editor = {Verlag, Wichmann}, isbn = {ISBN 978-3-87907-738-0}, journal = {22. Internationale Geodätische Woche Obergurgl 2022}, pages = {188-179}, title = {SLAM für die Echtzeit-Erfassung von Innenräumen }, year = 2023 }
4. Haala, N., Zhang, W., Joachim, L., Skuddis, D., Abolhasani, S., Schwieger, V., & Soergel, U. (2023). Zum Potenzial von SLAM-Verfahren für geodätische Echtzeit-Messaufgaben. Allgemeine Vermessungsnachrichten (Avn), 163–172. https://gispoint.de/artikelarchiv/avn/2023/avn-ausgabe-052023/7879-zum-potenzial-von-slam-verfahren-fuer-geodaetische-echtzeit-messaufgaben.html
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  @article{haala2023potenzial, abstract = {Simultaneous Localization and Mapping (SLAM) zielt auf die Echtzeiterfassung einer Umgebungskarte mittels einer mobilen Roboterplattform, wobei gleichzeitig die Lokalisierung des Roboters bezüglich seiner Umgebung erfolgt. Zu diesem Zweck kombiniert der Roboter Navigationssensoren, wie IMU oder Odometer, mit Sensoren, wie Laserscanner und/oder monokulare, Stereo- oder RGB-D-Kameras, zur Erfassung der Umgebung. Analog zum bekannten Structure-from-Motion-Verfahren basiert die simultane Bestimmung der Aufnahmepose und der 3D-Karte der Umgebung auf einer photogrammetrischen Bündelblockausgleichung. Hierzu werden zugeordnete Bildpunkte und/oder Laserscans zwischen den jeweiligen Aufnahmen genutzt. SLAM-Verfahren haben mittlerweile einen beachtlichen Entwicklungsstand erreicht. Sie kommen in immer mehr praktischen Anwendungen zum Einsatz und können zunehmend auch für ingenieurgeodätische Messaufgaben genutzt werden. Von besonderem Interesse sind dabei Echtzeitanwendungen. In diesem Beitrag wird das Potenzial existierender SLAM-Verfahren für geodätische Aufgaben am Beispiel aktueller Projekte, wie der Erfassung von Baustellen mittels auf einem Kran montierten Kameras und der mobilen 3D-Kartierung von Innenräumen, demonstriert und diskutiert.}, author = {Haala, Norbert and Zhang, Wei and Joachim, Lena and Skuddis, David and Abolhasani, Sahar and Schwieger, Volker and Soergel, Uwe}, journal = {Allgemeine Vermessungsnachrichten (avn)}, month = {05}, pages = {163-172}, title = {Zum Potenzial von SLAM-Verfahren für geodätische Echtzeit-Messaufgaben}, url = {https://gispoint.de/artikelarchiv/avn/2023/avn-ausgabe-052023/7879-zum-potenzial-von-slam-verfahren-fuer-geodaetische-echtzeit-messaufgaben.html}, year = 2023 }
5. Hierholz, A., Gienger, A., & Sawodny, O. (2023). Cooperative Time-Optimal Trajectory Generation for a Heterogeneous Group of Redundant Mobile Manipulators. Proceedings of the 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 1296–1301. https://doi.org/10.1109/AIM46323.2023.10196138
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  BibTeX
  @inproceedings{hierholz2023cooperative, abstract = {In order to increase productivity and resource efficiency in the construction sector, new approaches are being pursued to automate tasks in the interior fitting of existing buildings. For this purpose, a heterogeneous group of redundant mobile manipulators is used for the time-consuming exact positioning of workpieces. The focus lies hereby on cooperative time-optimal trajectory generation for mobile manipulators. To minimize the effort for modeling and formulating the optimal control problem for the different robots, a unified approach based on the Unified Robotics Description Format is developed. The optimal control problem considers the equations of motion of the robots and existing kinematic and dynamic constraints as well as collisions. To enable cooperative trajectories, additional kinematic constraints are imposed on the poses of the end effectors of each mobile manipulator. Different numerical methods, degrees of model abstraction and cost-functionals are investigated with respect to the generated trajectories and the required computation time towards real-time capability for future adaptive trajectory generation using sensor feedback.}, author = {Hierholz, Alice and Gienger, Andreas and Sawodny, Oliver}, booktitle = {Proceedings of the 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}, doi = {10.1109/AIM46323.2023.10196138}, issn = {2159-6255}, month = {06}, pages = {1296-1301}, title = {Cooperative Time-Optimal Trajectory Generation for a Heterogeneous Group of Redundant Mobile Manipulators}, year = 2023 }
6. Koelle, M., Walter, V., Schmohl, S., & Soergel, U. (2023). LEARNING ON THE EDGE: BENCHMARKING ACTIVE LEARNING FOR THE SEMANTIC SEGMENTATION OF ALS POINT CLOUDS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, 945–952. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-945-2023
  - BibTeX
  BibTeX
  @article{isprs-annals-X-1-W1-2023-945-2023, author = {Koelle, M. and Walter, V. and Schmohl, S. and Soergel, U.}, doi = {10.5194/isprs-annals-X-1-W1-2023-945-2023}, journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {945--952}, title = {LEARNING ON THE EDGE: BENCHMARKING ACTIVE LEARNING FOR THE SEMANTIC SEGMENTATION OF ALS POINT CLOUDS}, url = {https://isprs-annals.copernicus.org/articles/X-1-W1-2023/945/2023/}, volume = {X-1/W1-2023}, year = 2023 }
7. Parlapanis, C., Frontull, M., & Sawodny, O. (2023). Feed-Forward Control of a Construction Vehicle’s Hydro-Mechanical Powertrain to Prevent Engine Stalling. Proceedings of the 2023 IEEE Conference on Systems, Man, and Cybernetics (SMC).
  - BibTeX
  BibTeX
  @inproceedings{christosparlapanis2023feedforward, author = {Parlapanis, Christos and Frontull, Matthias and Sawodny, Oliver}, booktitle = {Proceedings of the 2023 IEEE Conference on Systems, Man, and Cybernetics (SMC)}, title = {Feed-Forward Control of a Construction Vehicle's Hydro-Mechanical Powertrain to Prevent Engine Stalling}, year = 2023 }
8. Zhang, X., Lin, D., Xue, R., & Soergel, U. (2023). TARGET-GUIDED LEARNING FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023, 1693–1698. https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1693-2023
  - BibTeX
  BibTeX
  @article{isprs-archives-XLVIII-1-W2-2023-1693-2023, author = {Zhang, X. and Lin, D. and Xue, R. and Soergel, U.}, doi = {10.5194/isprs-archives-XLVIII-1-W2-2023-1693-2023}, journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {1693--1698}, title = {TARGET-GUIDED LEARNING FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS}, url = {https://isprs-archives.copernicus.org/articles/XLVIII-1-W2-2023/1693/2023/}, volume = {XLVIII-1/W2-2023}, year = 2023 }
2022
1. Zhang, X., Xue, R., Kölle, M., & Sörgel, U. (2022). Point surfel transformer network for semantic segmentation of large-scale ALS point clouds. 42. Wissenschaftlich-Technische Jahrestagung Der DGPF, 5.-6. Oktober 2022 in Dresden, 290–296. https://doi.org/10.24407/KXP:1796046701
  - BibTeX
  BibTeX
  @inproceedings{TIBKAT:1796046701, address = {Stuttgart}, author = {Zhang, Xinlong and Xue, Ruihang and Kölle, Michael and Sörgel, Uwe}, booktitle = {42. Wissenschaftlich-Technische Jahrestagung der DGPF, 5.-6. Oktober 2022 in Dresden}, doi = {10.24407/KXP:1796046701}, pages = {290-296}, publisher = {Geschäftsstelle der DGPF; }, title = {Point surfel transformer network for semantic segmentation of large-scale ALS point clouds}, url = {https://www.tib.eu/de/suchen/id/TIBKAT%3A1796046701}, year = 2022 }

DATA SETS

2026
1. Hierholz, A., Röhm, E., Gienger, A., & Sawodny, O. (2026). Replication Data for: Distributed Trajectory Generation based on ADMM for Collaborative Workpiece Transport with Mobile Robots [DaRUS]. https://doi.org/10.18419/DARUS-5722
  - BibTeX
  BibTeX
  @dataset{DARUS-5722_2026, author = {Hierholz, Alice and Röhm, Emil and Gienger, Andreas and Sawodny, Oliver}, doi = {10.18419/DARUS-5722}, publisher = {DaRUS}, title = {{Replication Data for: Distributed Trajectory Generation based on ADMM for Collaborative Workpiece Transport with Mobile Robots}}, url = {https://doi.org/10.18419/DARUS-5722}, version = {V1}, year = 2026 }
2025
1. Balangé, L., Kerekes, G.-A., Frolow, R., Yang, Y., Abolhasani, S., & Schwieger, V. (2025). Point Clouds of the livMatS Biomimetic Shell at Various Stages of the Construction Process. https://doi.org/10.18419/darus-5093
  - BibTeX
  BibTeX
  @dataset{balange2025point, abstract = {The data set contains the point clouds of different construction stages of the of the building demonstrator 'livMatS Biomimetic Shell' during the manufacturing process, as well as a point cloud of the pavilion after construction of the shell. The data is named according to the numbering of the most recently built element, which does not necessarily correspond to chronological order.The first eight files, each with a two-digit number at the beginning of the file name, represent the ID of the cassette built for the respective scan. The FIT_gesamt file represents the final scan after all cassettes have been assembled. }, affiliation = {Balangé, Laura/University of Stuttgart, Kerekes, Gabriel/University of Stuttgart, Frolow, Rudolf/University of Stuttgart, Yang, Yihui/Technical University of Munich, Abolhasani, Sahar/University of Stuttgart, Schwieger, Volker/University of Stuttgart}, author = {Balangé, Laura and Kerekes, Gabriel-Adrian and Frolow, Rudolf and Yang, Yihui and Abolhasani, Sahar and Schwieger, Volker}, doi = {10.18419/darus-5093}, howpublished = {Dataset}, orcid-numbers = {Balangé, Laura/https://orcid.org/0000-0003-3922-1989, Kerekes, Gabriel/https://orcid.org/0000-0002-9037-0499, Frolow, Rudolf/https://orcid.org/0009-0006-1789-978X, Yang, Yihui/https://orcid.org/0000-0002-0646-1073, Abolhasani, Sahar/https://orcid.org/0009-0000-2006-5010, Schwieger, Volker/https://orcid.org/0000-0001-9055-9809}, title = {Point Clouds of the livMatS Biomimetic Shell at Various Stages of the Construction Process}, year = 2025 }
2. Hierholz, A., Gienger, A., & Sawodny, O. (2025). Replication Data for: Improving Data-based Trajectory Generation by Quadratic Programming for Redundant Mobile Manipulators [DaRUS]. https://doi.org/10.18419/DARUS-5029
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-5029, author = {Hierholz, Alice and Gienger, Andreas and Sawodny, Oliver}, doi = {10.18419/DARUS-5029}, publisher = {DaRUS}, title = {Replication Data for: Improving Data-based Trajectory Generation by Quadratic Programming for Redundant Mobile Manipulators}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/DARUS-5029}, year = 2025 }