TEASER++ is a fast and certifiably-robust point cloud registration library written in C++, with Python and MATLAB bindings.
Left: correspondences generated by 3DSmoothNet (green and red lines represent the inlier and outlier correspondences according to the ground truth respectively). Right: alignment estimated by TEASER++ (green dots represent inliers found by TEASER++).
TEASER++ can solve the rigid body transformation problem between two point clouds in 3D. It performs well even if the input correspondences have an extremely large number of outliers. For a short conceptual introduction, check out our video. For more information, please refer to our papers:
- H. Yang, J. Shi, and L. Carlone, "TEASER: Fast and Certifiable Point Cloud Registration,". arXiv:2001.07715 [cs, math], Jan. 2020. (pdf)
- H. Yang and L. Carlone, “A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates,” in Robotics: Science and Systems (RSS), 2019. (pdf)
If you find this library helpful or use it in your projects, please cite:
@article{Yang20tro-teaser,
title={{TEASER: Fast and Certifiable Point Cloud Registration}},
author={H. Yang and J. Shi and L. Carlone},
journal={{IEEE} Trans. Robotics},
pdf={https://arxiv.org/pdf/2001.07715.pdf},
Year = {2020}
}If you are interested in more works from us, please visit our lab page here.
- Installation
- Usage
- API Documentation
Other publications related to TEASER include:
- H. Yang and L. Carlone, “A quaternion-based certifiably optimal solution to the Wahba problem with outliers,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019, pp. 1665–1674. (pdf)
- H. Yang, P. Antonante, V. Tzoumas, and L. Carlone, “Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection,” IEEE Robotics and Automation Letters (RA-L), 2020. (pdf)
This work was partially funded by ARL DCIST CRA W911NF-17-2-0181, ONR RAIDER N00014-18-1-2828, Lincoln Laboratory “Resilient Perception in Degraded Environments”, and the Google Daydream Research Program.