Generalized Proximal Methods for Pose Graph Optimization

Taosha Fan*, Todd Murphey

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we generalize proximal methods that were originally designed for convex optimization on normed vector space to non-convex pose graph optimization (PGO) on special Euclidean groups, and show that our proposed generalized proximal methods for PGO converge to first-order critical points. Furthermore, we propose methods that significantly accelerate the rates of convergence almost without loss of any theoretical guarantees. In addition, our proposed methods can be easily distributed and parallelized with no compromise of efficiency. The efficacy of this work is validated through implementation on simultaneous localization and mapping (SLAM) and distributed 3D sensor network localization, which indicate that our proposed methods are a lot faster than existing techniques to converge to sufficient accuracy for practical use.

Original languageEnglish (US)
Title of host publicationRobotics Research - The 19th International Symposium ISRR
EditorsTamim Asfour, Eiichi Yoshida, Jaeheung Park, Henrik Christensen, Oussama Khatib
PublisherSpringer Nature
Pages393-409
Number of pages17
ISBN (Print)9783030954581
DOIs
StatePublished - 2022
Event17th International Symposium of Robotics Research, ISRR 2019 - Hanoi, Viet Nam
Duration: Oct 6 2019Oct 10 2019

Publication series

NameSpringer Proceedings in Advanced Robotics
Volume20 SPAR
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Conference

Conference17th International Symposium of Robotics Research, ISRR 2019
Country/TerritoryViet Nam
CityHanoi
Period10/6/1910/10/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Engineering (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Applied Mathematics

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