Libpointmatcher

Libpointmatcher

A modular library for point cloud registrations

Libpointmatcher is a library that implements the Iterative Closest Point (ICP) algorithm for alignment of point clouds. It supports both point-to-point and point-to-plane ICP. With the former, it is able to solve not only for a rigid transform, but also for a scale change between the clouds (that is, a similarity transform).

The library implements a set of filters to help denoise and subsample the input point clouds. It supports a variety of file types and it can be configured via both YAML files and an in-memory API. Libpointmatcher is written in C++ and fast enough for real-time application.

Note: This project is ongoing and was started during a formal employment at ETH Zurich, within the ASL.

See it on GitHub

  • Main Investigator and scientific lead: François Pomerleau
  • Scientific Support: Francis Colas, Stéphane Magnenat

Documentation

Publications

  1. Chahine, G., Vaidis, M., Pomerleau, F., & Pradalier, C. (2020). Mapping in unstructured natural environment: A sensor fusion framework for wearable sensor suites. Journal of Intelligent & Robotic Systems. Bibtex source
  2. Kubelka, V., Dandurand, P., Babin, P., Giguère, P., & Pomerleau, F. (2020). Radio propagation models for differential GNSS based on dense point clouds. Journal of Field Robotics. Bibtex source
  3. Tremblay, J.-F., Béland, M., Pomerleau, F., Gagnon, R., & Giguère, P. (2020). Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations. Journal of Field Robotics. https://doi.org/10.1002/rob.21980 Bibtex source
  4. Labussière, M., Laconte, J., & Pomerleau, F. (2020). Geometry Preserving Sampling Method based on Spectral Decomposition for Large-scale Environments. Frontiers in Robotics and AI – Multi-Robot Systems, 7, 134. https://doi.org/10.3389/frobt.2020.572054 Bibtex source
  5. Pradalier, C., Aravecchia, S., & Pomerleau, F. (2019). Multi-session lake-shore monitoring in visually challenging conditions. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics. Bibtex source
  6. Dandurand, P., Babin, P., Kubelka, V., Giguère, P., & Pomerleau, F. (2019). Predicting GNSS satellite visibility from dense point clouds. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics. PDF Bibtex source
  7. Babin, P., Dandurand, P., Kubelka, V., Giguère, P., & Pomerleau, F. (2019). Large-scale 3D Mapping of Subarctic Forests. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics. PDF Slides Bibtex source
  8. Tremblay, J.-F., Béland, M., Pomerleau, F., Gagnon, R., & Giguère, P. (2019). Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics. PDF Slides Bibtex source
  9. Landry, D., Pomerleau, F., & Giguère, P. (2019). CELLO-3D: Estimating the Covariance of ICP in the Real World. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). PDF Bibtex source
  10. Babin, P., Giguère, P., & Pomerleau, F. (2019). Analysis of Robust Functions for Registration Algorithms. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). PDF Bibtex source
  11. Labussière, M., Laconte, J., & Pomerleau, F. (2018). Geometry Preserving Sampling Method based on Spectral Decomposition for 3D Registration. ArXiv Preprint ArXiv:1810.01666. Bibtex source
  12. Babin, P., Pomerleau, F., & Giguère, P. (2018). Improving the robustness of registration algorithm in complex environments. Colloque du regroupement FRQNT-REPARTI. Bibtex source
  13. Landry, D., Pomerleau, F., & Giguère, P. (2018). Data-driven covariance estimation of the iterative closest point algorithm. Rendez-vous en intelligence artificielle de Québec. Bibtex source
  14. McGarey, P., Pomerleau, F., & Barfoot, T. D. (2016). System design of a tethered robotic explorer (TReX) for 3D mapping of steep terrain and harsh environments. Proceedings of the Conference on Field and Service Robotics (FSR). Springer Tracts in Advanced Robotics, 113, 267–281. Bibtex source
  15. Krüsi, P., Bücheler, B., Pomerleau, F., Schwesinger, U., Siegwart, R., & Furgale, P. (2015). Lighting-invariant adaptive route following using iterative closest point matching. Journal of Field Robotics, 32(4), 534–564. Bibtex source
  16. Hitz, G., Pomerleau, F., Colas, F., & Siegwart, R. (2015). Relaxing the planar assumption: 3D state estimation for an autonomous surface vessel. International Journal of Robotics Research, 34(13), 1604–1621. Bibtex source
  17. Pomerleau, F., Colas, F., & Siegwart, R. (2015). A Review of Point Cloud Registration Algorithms for Mobile Robotics. Foundations and Trends in Robotics, 4(1), 1–104. Bibtex source
  18. Pomerleau, F., Krüsi, P., Colas, F., Furgale, P., & Siegwart, R. (2014). Long-term 3D map maintenance in dynamic environments. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 3712–3719. Bibtex source
  19. Pomerleau, F. (2013). Applied Registration for Robotics: Methodology and Tools for ICP-like Algorithms [PhD thesis]. Eidgenössische Technische Hochschule (ETH) Zurich. Bibtex source
  20. Pomerleau, F., Colas, F., Siegwart, R., & Magnenat, S. (2013). Comparing ICP variants on real-world data sets: Open-source library and experimental protocol. Autonomous Robots, 34(3), 133–148. Bibtex source

Results