認定可能なロボット知覚のためのBurer-Monteiro法の概要
An Overview of the Burer-Monteiro Method for Certifiable Robot Perception
ロボット知覚問題をリアルタイムで認定可能な最適性で解くBurer-Monteiro法について、文献を統合し、LICQの役割を明らかにし、実践的な考慮事項を共有する。
著者: Alan Papalia, Yulun Tian, David M. Rosen, Jonathan P. How, John J. Leonard
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
