StachnissLab

Short CV

Saurabh Gupta is a PhD student at the Photogrammetry Lab at the University of Bonn since November 2023. He received his master’s degree at the Institute of Geodesy and Geoinformation in 2023 working on LiDAR-based place recognition and loop closing.

Research Interests

• SLAM
• Place recognition

Teaching

• 2023/2024 – Modern C++ for Robotics and Computer Vision

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

• L. Wiesmann, E. Marks, S. Gupta, T. Guadagnino, J. Behley, and C. Stachniss, “Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories,” Arxiv preprint, vol. arXiv:2412.11760, 2024.
  [BibTeX] [PDF]

  @article{wiesmann2024arxiv,
  author = {L. Wiesmann and E. Marks and S. Gupta and T. Guadagnino and J. Behley and C. Stachniss},
  title = {{Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories}},
  journal = arxiv,
  year = 2024,
  volume = {arXiv:2412.11760},
  url = {https://arxiv.org/pdf/2412.11760}
  }

• 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}
  }

• 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}
  }