Certifiable Outlier-Robust Geometric Perception

About

This repository holds the implementation for certifiably solving outlier-robust geometric perception problems to global optimality. The certifiable outlier-robust geometric perception framework contains two main modules:

• A sparse semidefinite programming relaxation (SSR) scheme that relaxes nonconvex outlier-robust perception problems into convex semidefinite programs (SDPs); and

• A novel SDP solver, called STRIDE, that solves the generated SDPs at an unprecedented scale and accuracy.

We proposed a preliminary version of the SSR scheme in our NeurIPS 2020 paper, and released a certifier (that certifies if a given estimate is optimal) based on Douglas-Rachford Splitting (DRS). Please switch to the NeurIPS2020 branch of this repo to checkout the NeurIPS2020 implementation.

If you find this library helpful or use it in your projects, please cite:

@article{Yang21arXiv-certifiableperception,
  title={Certifiable Outlier-Robust Geometric Perception: Exact Semidefinite Relaxations and Scalable Global Optimization},
  author={Yang, Heng and Carlone, Luca},
  journal={arXiv preprint arXiv:2109.03349},
  year={2021}
}

Documentation

Coming soon.