Benedikt Mersch

PhD Student
Contact:
Email: mersch@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 27 11
Office: Nussallee 15, 1. OG, room 1.012
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Profiles: Google Scholar | Github

Research Interests

  • SLAM
  • LiDAR Moving Object Segmentation
  • Behavior Estimation

Short CV

Benedikt Mersch is a Ph.D. student at the Photogrammetry & Robotics Lab at the University of Bonn since July 2020. He received his master’s degree in Electrical Engineering at the Hamburg University of Technology in December 2019.

Teaching

  • Techniques for Self-Driving Cars, 2024/2025
  • Techniques for Self-Driving Cars, 2023/2024
  • Techniques for Self-Driving Cars, 2022/2023
  • Techniques for Self-Driving Cars, 2021/2022
  • Mobile Sensing and Robotics Project, 2020/2021
  • Techniques for Self-Driving Cars, 2020/2021

Code releases

Publications

2025

  • T. Guadagnino, B. Mersch, S. Gupta, I. Vizzo, G. Grisetti, and C. Stachniss, “KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities,” arXiv Preprint, vol. arXiv:2503.12660, 2025.
    [BibTeX] [PDF] [Code]

    Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the Keep It Small and Simple (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.

    @article{guadagnino2025arxiv,
author = {T. Guadagnino and B. Mersch and S. Gupta and I. Vizzo and G. Grisetti and C. Stachniss},
title = {{KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities}},
journal = arxiv,
year = 2025,
volume = {arXiv:2503.12660},
url = {https://arxiv.org/pdf/2503.12660},
codeurl = {https://github.com/PRBonn/kiss-slam},
abstract = {Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the Keep It Small and Simple (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.},
}

2024

  • T. Guadagnino, B. Mersch, I. Vizzo, S. Gupta, M. V. R. Malladi, L. Lobefaro, G. Doisy, and C. Stachniss, “Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces,” arXiv Preprint, vol. arXiv:2410.10277, 2024.
    [BibTeX] [PDF] [Code] 
    @article{guadagnino2024arxiv,
author = {Guadagnino, T. and Mersch, B. and Vizzo, I. and Gupta, S. and Malladi, M.V.R. and Lobefaro, L. and Doisy, G. and Stachniss, C.},
title = {{Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces}},
journal = arxiv,
year = {2024},
volume = {arXiv:2410.10277},
url = {https://arxiv.org/pdf/2410.10277},
codeurl = {https://github.com/PRBonn/kinematic-icp}
}

  • H. Lim, S. Jang, B. Mersch, J. Behley, H. Myung, and C. Stachniss, “HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2024. doi:10.1109/IROS58592.2024.10801938
    [BibTeX] [PDF] 
    @inproceedings{lim2024iros,
author = {H. Lim and S. Jang and B. Mersch and J. Behley and H. Myung and C. Stachniss},
title = {{HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors}},
booktitle = iros,
year = 2024,
doi = {10.1109/IROS58592.2024.10801938}
}

  • L. Nunes, R. Marcuzzi, B. Mersch, J. Behley, and C. Stachniss, “Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. doi:10.1109/CVPR52733.2024.01399
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{nunes2024cvpr,
author = {L. Nunes and R. Marcuzzi and B. Mersch and J. Behley and C. Stachniss},
title = {{Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion}},
booktitle = cvpr,
year = 2024,
doi = {10.1109/CVPR52733.2024.01399},
codeurl = {https://github.com/PRBonn/LiDiff},
videourl = {https://youtu.be/XWu8svlMKUo}
}

  • I. Hroob, B. Mersch, C. Stachniss, and M. Hanheide, “Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization,” IEEE Robotics and Automation Letters (RA-L), vol. 9, iss. 4, p. 3546–3553, 2024. doi:10.1109/LRA.2024.3368236
    [BibTeX] [PDF] [Code] [Video] 
    @article{hroob2024ral,
author = {I. Hroob and B. Mersch and C. Stachniss and M. Hanheide},
title = {{Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization}},
journal = ral,
volume = {9},
number = {4},
pages = {3546--3553},
year = 2024,
doi = {10.1109/LRA.2024.3368236},
videourl = {https://youtu.be/aRLStFQEXbc},
codeurl = {https://github.com/ibrahimhroob/SPS}
}

  • S. Gupta, T. Guadagnino, B. Mersch, I. Vizzo, and C. Stachniss, “Effectively Detecting Loop Closures using Point Cloud Density Maps,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024. doi:10.1109/ICRA57147.2024.10610962
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{gupta2024icra,
author = {S. Gupta and T. Guadagnino and B. Mersch and I. Vizzo and C. Stachniss},
title = {{Effectively Detecting Loop Closures using Point Cloud Density Maps}},
booktitle = icra,
year = 2024,
doi = {10.1109/ICRA57147.2024.10610962},
codeurl = {https://github.com/PRBonn/MapClosures},
videourl = {https://youtu.be/BpwR_aLXrNo}
}

  • M. Zeller, V. S. Sandhu, B. Mersch, J. Behley, M. Heidingsfeld, and C. Stachniss, “Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds,” IEEE Trans. on Robotics (TRO), vol. 40, pp. 2357-2372, 2024. doi:10.1109/TRO.2023.3338972
    [BibTeX] [PDF] [Video] 
    @article{zeller2024tro,
author = {M. Zeller and Sandhu, V.S. and B. Mersch and J. Behley and M. Heidingsfeld and C. Stachniss},
title = {{Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds}},
journal = tro,
year = {2024},
volume = {40},
doi = {10.1109/TRO.2023.3338972},
pages = {2357-2372},
videourl = {https://www.youtube.com/watch?v=v-iXbJEcqPM}
}

2023

  • I. Vizzo, B. Mersch, L. Nunes, L. Wiesmann, T. Guadagnino, and C. Stachniss, “Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition,” in Proc. of the Intl. Conf. on Intelligent Transportation Systems Workshops, 2023.
    [BibTeX] [PDF] [Code] 
    @inproceedings{vizzo2023itcsws,
author = {I. Vizzo and B. Mersch and L. Nunes and L. Wiesmann and T. Guadagnino and C. Stachniss},
title = {{Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition}},
booktitle = {Proc. of the Intl. Conf. on Intelligent Transportation Systems Workshops},
year = 2023,
codeurl = {https://github.com/ipb-car/meta-workspace},
note = {accepted}
}

  • B. Mersch, T. Guadagnino, X. Chen, I. Vizzo, J. Behley, and C. Stachniss, “Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 8, pp. 5180-5187, 2023. doi:10.1109/LRA.2023.3292583
    [BibTeX] [PDF] [Code] [Video] 
    @article{mersch2023ral,
author = {B. Mersch and T. Guadagnino and X. Chen and I. Vizzo and J. Behley and C. Stachniss},
title = {{Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation}},
journal = ral,
volume = {8},
number = {8},
pages = {5180-5187},
year = 2023,
issn = {2377-3766},
doi = {10.1109/LRA.2023.3292583},
videourl = {https://youtu.be/aeXhvkwtDbI},
codeurl = {https://github.com/PRBonn/MapMOS}
}

  • H. Lim, L. Nunes, B. Mersch, X. Chen, J. Behley, H. Myung, and C. Stachniss, “ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes,” in Proc. of Robotics: Science and Systems (RSS), 2023.
    [BibTeX] [PDF] 
    @inproceedings{lim2023rss,
author = {H. Lim and L. Nunes and B. Mersch and X. Chen and J. Behley and H. Myung and C. Stachniss},
title = {{ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes}},
booktitle = rss,
year = 2023
}

  • M. Zeller, V. S. Sandhu, B. Mersch, J. Behley, M. Heidingsfeld, and C. Stachniss, “Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Video] 
    @inproceedings{zeller2023icra,
author = {M. Zeller and V.S. Sandhu and B. Mersch and J. Behley and M. Heidingsfeld and C. Stachniss},
title = {{Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds}},
booktitle = icra,
year = 2023,
videourl = {https://youtu.be/dTDgzWIBgpE}
}

  • I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss, “KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 2, pp. 1-8, 2023. doi:10.1109/LRA.2023.3236571
    [BibTeX] [PDF] [Code] [Video] 
    @article{vizzo2023ral,
author = {Vizzo, Ignacio and Guadagnino, Tiziano and Mersch, Benedikt and Wiesmann, Louis and Behley, Jens and Stachniss, Cyrill},
title = {{KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way}},
journal = ral,
pages = {1-8},
doi = {10.1109/LRA.2023.3236571},
volume = {8},
number = {2},
year = {2023},
codeurl = {https://github.com/PRBonn/kiss-icp},
videourl = {https://youtu.be/h71aGiD-uxU}
}

2022

  • I. Vizzo, B. Mersch, R. Marcuzzi, L. Wiesmann, J. Behley, and C. Stachniss, “Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 8534-8541, 2022. doi:10.1109/LRA.2022.3187255
    [BibTeX] [PDF] [Code] [Video] 
    @article{vizzo2022ral,
author = {I. Vizzo and B. Mersch and R. Marcuzzi and L. Wiesmann and J. Behley and C. Stachniss},
title = {Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments},
journal = ral,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2022ral-iros.pdf},
codeurl = {https://github.com/PRBonn/make_it_dense},
year = {2022},
volume = {7},
number = {3},
pages = {8534-8541},
doi = {10.1109/LRA.2022.3187255},
videourl = {https://youtu.be/NVjURcArHn8}
}

  • B. Mersch, X. Chen, I. Vizzo, L. Nunes, J. Behley, and C. Stachniss, “Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, p. 7503–7510, 2022. doi:10.1109/LRA.2022.3183245
    [BibTeX] [PDF] [Code] [Video] 
    @article{mersch2022ral,
author = {B. Mersch and X. Chen and I. Vizzo and L. Nunes and J. Behley and C. Stachniss},
title = {{Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions}},
journal = ral,
year = 2022,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2022ral.pdf},
volume = {7},
number = {3},
pages = {7503--7510},
doi = {10.1109/LRA.2022.3183245},
codeurl = {https://github.com/PRBonn/4DMOS},
videourl = {https://youtu.be/5aWew6caPNQ}
}

  • X. Chen, B. Mersch, L. Nunes, R. Marcuzzi, I. Vizzo, J. Behley, and C. Stachniss, “Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 6107-6114, 2022. doi:10.1109/LRA.2022.3166544
    [BibTeX] [PDF] [Code] [Video] 
    @article{chen2022ral,
author = {X. Chen and B. Mersch and L. Nunes and R. Marcuzzi and I. Vizzo and J. Behley and C. Stachniss},
title = {{Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation}},
journal = ral,
year = 2022,
volume = 7,
number = 3,
pages = {6107-6114},
url = {https://arxiv.org/pdf/2201.04501},
issn = {2377-3766},
doi = {10.1109/LRA.2022.3166544},
codeurl = {https://github.com/PRBonn/auto-mos},
videourl = {https://youtu.be/3V5RA1udL4c}
}

2021

  • B. Mersch, X. Chen, J. Behley, and C. Stachniss, “Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks,” in Proc. of the Conf. on Robot Learning (CoRL), 2021.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{mersch2021corl,
author = {B. Mersch and X. Chen and J. Behley and C. Stachniss},
title = {{Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks}},
booktitle = corl,
year = {2021},
url = {https://www.ipb.uni-bonn.de/pdfs/mersch2021corl.pdf},
codeurl = {https://github.com/PRBonn/point-cloud-prediction},
videourl = {https://youtu.be/-pSZpPgFAso}
}

  • B. Mersch, T. Höllen, K. Zhao, C. Stachniss, and R. Roscher, “Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2021.
    [BibTeX] [PDF] [Video] 
    @inproceedings{mersch2021iros,
title = {{Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks}},
author = {B. Mersch and T. H\"ollen and K. Zhao and C. Stachniss and R. Roscher},
booktitle = iros,
year = {2021},
videourl = {https://youtu.be/5RRGWUn4qAw},
url = {https://www.ipb.uni-bonn.de/pdfs/mersch2021iros.pdf}
}

  • X. Chen, S. Li, B. Mersch, L. Wiesmann, J. Gall, J. Behley, and C. Stachniss, “Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 6529-6536, 2021. doi:10.1109/LRA.2021.3093567
    [BibTeX] [PDF] [Code] [Video] 
    @article{chen2021ral,
title = {{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
author = {X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss},
year = {2021},
volume = 6,
issue = 4,
pages = {6529-6536},
journal = ral,
url = {https://www.ipb.uni-bonn.de/pdfs/chen2021ral-iros.pdf},
codeurl = {https://github.com/PRBonn/LiDAR-MOS},
videourl = {https://youtu.be/NHvsYhk4dhw},
doi = {10.1109/LRA.2021.3093567},
issn = {2377-3766}
}