An Overview of the Burer-Monteiro Method for Certifiable Robot Perception
<|reference_start|>An Overview of the Burer-Monteiro Method for Certifiable Robot Perception: This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-rank structure of typical semidefinite programs to dramatically reduce the computational cost of performing optimization. This paper discusses BM in certifiable perception, with three main objectives: (i) to consolidate information from the literature into a unified presentation, (ii) to elucidate the role of the linear independence constraint qualification (LICQ), a concept not yet well-covered in certifiable perception literature, and (iii) to share practical considerations that are discussed among practitioners but not thoroughly covered in the literature. Our general aim is to offer a practical primer for applying BM towards certifiable perception.<|reference_end|>
@article{papalia2024an,
    title={An Overview of the Burer-Monteiro Method for Certifiable Robot
  Perception},
    author={Alan Papalia, Yulun Tian, David M. Rosen, Jonathan P. How, John J.
  Leonard},
    journal={arXiv preprint arXiv:2410.00117},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00117},
    primaryClass={cs.RO cs.CV cs.LG}
}