Publications
Publication List with Images
2026 |
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Ellensohn, Björn; Rueckert*, Elmar; Rauch*, Christian EliGSiR: Continual RGB-D Mapping with Gaussian Splatting under Bounded Compute Online 2026. Abstract | Links | BibTeX | Tags: computer vision, Gaussian Splatting, mobile navigation, SLAM @online{Ellensohn2026,Conventional 3D Gaussian Splatting assumes a closed set of observations and long optimization schedules. Continual RGB-D mapping in contrast poses the problem that new observations arrive online, while previously reconstructed regions must be preserved. We present EliGSiR (Evidence-guided Load-adaptive Incremental Gaussian Splatting with Image Replay), a continual Gaussian mapper that controls how the available optimization budget is used as the reconstruction evolves. Map-Guided View Scheduling filters redundant incoming views and reconsiders retained views according to the current state of the map. Load-Adaptive Fidelity adjusts supervision resolution to the current mapping load instead of following a fixed resolution schedule. Targeted Geometry Growth separates depth supervision from Gaussian creation and adds geometric capacity only where repeated RGB-D observations indicate missing or misplaced structure. Together, these mechanisms adapt which views are optimized, how much image detail is used, and where the representation grows while mapping remains active. We evaluate EliGSiR on Replica, TUM RGB-D, ScanNet++, and real RGB-D sensor sequences, considering both the final reconstruction and the map available throughout acquisition. On TUM RGB-D fr3/long_office_household, EliGSiR reaches 21.52 dB with the same ground-truth mapping poses used by the controlled baselines, compared with 19.42 dB for SplaTAM. In the tracked-pose comparison, EliGSiR with live ORB-SLAM3 poses reaches 23.02 dB in 155.5 s, compared with 20.10 dB in 230.9 s for CaRtGS using its native tracker. We further evaluate reconstruction throughout acquisition and show how EliGSiR adaptive view scheduling, supervision fidelity, and geometry growth improve the use of the available mapping budget. |  |
2025 |
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Nwankwo, Linus; Ellensohn, Bjoern; Dave, Vedant; Hofer, Peter; Forstner, Jan; Villneuve, Marlene; Galler, Robert; Rueckert, Elmar EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments Proceedings Article In: IEEE International Conference on Robotics and Automation (ICRA 2025)., 2025. Links | BibTeX | Tags: Autonomous Navigation, robotics, SLAM @inproceedings{Nwankwo2025, |  |
2023 |
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Nwankwo, Linus; Rueckert, Elmar Understanding why SLAM algorithms fail in modern indoor environments Proceedings Article In: International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). , pp. 186 - 194, Cham: Springer Nature Switzerland., 2023. Abstract | Links | BibTeX | Tags: mobile navigation, robotics, SLAM @inproceedings{Nwankwo2023,Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate SLAM algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) SLAM algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA SLAM algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm’s performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios. |  |
Nwankwo, Linus; Fritze, Clemens; Bartsch, Konrad; Rueckert, Elmar ROMR: A ROS-based Open-source Mobile Robot Journal Article In: HardwareX, vol. 15, pp. 1–29, 2023. Abstract | Links | BibTeX | Tags: Autonomous Navigation, mobile navigation, SLAM @article{Nwankwo2023b,Currently, commercially available intelligent transport robots that are capable of carrying up to 90kg of load can cost $5,000 or even more. This makes real-world experimentation prohibitively expensive, and limiting the applicability of such systems to everyday home or industrial tasks. Aside from their high cost, the majority of commercially available platforms are either closed-source, platform-specific, or use difficult-to-customize hardware and firmware. In this work, we present a low-cost, open-source and modular alternative, referred to herein as ”ROS-based open-source mobile robot (ROMR)”. ROMR utilizes off-the-shelf (OTS) components, additive manufacturing technologies, aluminium profiles, and a consumer hoverboard with high-torque brushless direct current (BLDC) motors. ROMR is fully compatible with the robot operating system (ROS), has a maximum payload of 90kg, and costs less than $1500. Furthermore, ROMR offers a simple yet robust framework for contextualizing simultaneous localization and mapping (SLAM) algorithms, an essential prerequisite for autonomous robot navigation. The robustness and performance of the ROMR were validated through realworld and simulation experiments. All the design, construction and software files are freely available online under the GNU GPL v3 license at https://doi.org/10.17605/OSF.IO/K83X7. A descriptive video of ROMR can be found at https://osf.io/ku8ag. |  |
Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Thinh Deep Reinforcement Learning for Autonomous Navigation in Intralogistics Conference Workshop paper at the European Control Conference (ECC)., 2023. Abstract | Links | BibTeX | Tags: Autonomous Navigation, Deep Learning, mobile navigation, SLAM @conference{Yadav2023,Even with several advances in autonomous mobile robots, navigation in a highly dynamic environment still remains a challenge. Classical navigation systems, such as Simultaneous Localization and Mapping (SLAM), build a map of the environment and constructing maps of highly dynamic environments is impractical. Deep Reinforcement Learning (DRL) approaches have the ability to learn policies without knowledge of the maps or the transition models of the environment. The aim of our work is to investigate the potential of using DRL to control an autonomous mobile robot to dock with a load carrier. This paper presents an initial successful training result of the Soft Actor-Critic (SAC) algorithm, which can navigate a robot toward an open door only based on the 360° LiDAR observations. Ongoing work is using visual sensors for load carrier docking. |  |
Compact List without Images
Journal Articles |
Nwankwo, Linus; Fritze, Clemens; Bartsch, Konrad; Rueckert, Elmar ROMR: A ROS-based Open-source Mobile Robot Journal Article In: HardwareX, vol. 15, pp. 1–29, 2023. @article{Nwankwo2023b,Currently, commercially available intelligent transport robots that are capable of carrying up to 90kg of load can cost $5,000 or even more. This makes real-world experimentation prohibitively expensive, and limiting the applicability of such systems to everyday home or industrial tasks. Aside from their high cost, the majority of commercially available platforms are either closed-source, platform-specific, or use difficult-to-customize hardware and firmware. In this work, we present a low-cost, open-source and modular alternative, referred to herein as ”ROS-based open-source mobile robot (ROMR)”. ROMR utilizes off-the-shelf (OTS) components, additive manufacturing technologies, aluminium profiles, and a consumer hoverboard with high-torque brushless direct current (BLDC) motors. ROMR is fully compatible with the robot operating system (ROS), has a maximum payload of 90kg, and costs less than $1500. Furthermore, ROMR offers a simple yet robust framework for contextualizing simultaneous localization and mapping (SLAM) algorithms, an essential prerequisite for autonomous robot navigation. The robustness and performance of the ROMR were validated through realworld and simulation experiments. All the design, construction and software files are freely available online under the GNU GPL v3 license at https://doi.org/10.17605/OSF.IO/K83X7. A descriptive video of ROMR can be found at https://osf.io/ku8ag. |
Conferences |
Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Thinh Deep Reinforcement Learning for Autonomous Navigation in Intralogistics Conference Workshop paper at the European Control Conference (ECC)., 2023. @conference{Yadav2023,Even with several advances in autonomous mobile robots, navigation in a highly dynamic environment still remains a challenge. Classical navigation systems, such as Simultaneous Localization and Mapping (SLAM), build a map of the environment and constructing maps of highly dynamic environments is impractical. Deep Reinforcement Learning (DRL) approaches have the ability to learn policies without knowledge of the maps or the transition models of the environment. The aim of our work is to investigate the potential of using DRL to control an autonomous mobile robot to dock with a load carrier. This paper presents an initial successful training result of the Soft Actor-Critic (SAC) algorithm, which can navigate a robot toward an open door only based on the 360° LiDAR observations. Ongoing work is using visual sensors for load carrier docking. |
Proceedings Articles |
Nwankwo, Linus; Ellensohn, Bjoern; Dave, Vedant; Hofer, Peter; Forstner, Jan; Villneuve, Marlene; Galler, Robert; Rueckert, Elmar EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments Proceedings Article In: IEEE International Conference on Robotics and Automation (ICRA 2025)., 2025. @inproceedings{Nwankwo2025, |
Nwankwo, Linus; Rueckert, Elmar Understanding why SLAM algorithms fail in modern indoor environments Proceedings Article In: International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). , pp. 186 - 194, Cham: Springer Nature Switzerland., 2023. @inproceedings{Nwankwo2023,Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate SLAM algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) SLAM algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA SLAM algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm’s performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios. |
Online |
Ellensohn, Björn; Rueckert*, Elmar; Rauch*, Christian EliGSiR: Continual RGB-D Mapping with Gaussian Splatting under Bounded Compute Online 2026. @online{Ellensohn2026,Conventional 3D Gaussian Splatting assumes a closed set of observations and long optimization schedules. Continual RGB-D mapping in contrast poses the problem that new observations arrive online, while previously reconstructed regions must be preserved. We present EliGSiR (Evidence-guided Load-adaptive Incremental Gaussian Splatting with Image Replay), a continual Gaussian mapper that controls how the available optimization budget is used as the reconstruction evolves. Map-Guided View Scheduling filters redundant incoming views and reconsiders retained views according to the current state of the map. Load-Adaptive Fidelity adjusts supervision resolution to the current mapping load instead of following a fixed resolution schedule. Targeted Geometry Growth separates depth supervision from Gaussian creation and adds geometric capacity only where repeated RGB-D observations indicate missing or misplaced structure. Together, these mechanisms adapt which views are optimized, how much image detail is used, and where the representation grows while mapping remains active. We evaluate EliGSiR on Replica, TUM RGB-D, ScanNet++, and real RGB-D sensor sequences, considering both the final reconstruction and the map available throughout acquisition. On TUM RGB-D fr3/long_office_household, EliGSiR reaches 21.52 dB with the same ground-truth mapping poses used by the controlled baselines, compared with 19.42 dB for SplaTAM. In the tracked-pose comparison, EliGSiR with live ORB-SLAM3 poses reaches 23.02 dB in 155.5 s, compared with 20.10 dB in 230.9 s for CaRtGS using its native tracker. We further evaluate reconstruction throughout acquisition and show how EliGSiR adaptive view scheduling, supervision fidelity, and geometry growth improve the use of the available mapping budget. |