Large-scale cooperative 3D visual-inertial mapping in a Manhattan world

Chao X. Guo, Kourosh Sartipi, Ryan C. Dutoit, Georgios A. Georgiou, Ruipeng Li, John O'Leary, Esha D. Nerurkar, Joel A. Hesch, Stergios I. Roumeliotis

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

17 Scopus citations

Abstract

In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when the transformation between the users' starting poses is unknown. Specifically, we formulate CM as a constrained optimization problem, where each user's independently estimated trajectory and map are combined in a single map by imposing geometric constraints between commonly-observed point and line features. Furthermore, our formulation allows for modularity since new/old maps (or parts of them) can be easily added/removed with no impact on the remaining ones. Additionally, the proposed CM algorithm lends itself, for the most part, to parallel implementations, hence gaining in speed. Experimental results based on visual and inertial measurements collected from four users within two large buildings are used to assess the performance of the proposed CM algorithm.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1071-1078
Number of pages8
ISBN (Electronic)9781467380263
DOIs
StatePublished - Jun 8 2016
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: May 16 2016May 21 2016

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2016-June
ISSN (Print)1050-4729

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Country/TerritorySweden
CityStockholm
Period5/16/165/21/16

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