Distributed Real-time Cooperative Localization and Mapping Using an Uncertainty-Aware Expectation Maximization Approach

Dong, Jing; Nelson, Erik; Indelman, Vadim; Michael, Nathan; Dellaert, Frank

Title:

Distributed Real-time Cooperative Localization and Mapping Using an Uncertainty-Aware Expectation Maximization Approach

dc.contributor.author	Dong, Jing
dc.contributor.author	Nelson, Erik
dc.contributor.author	Indelman, Vadim
dc.contributor.author	Michael, Nathan
dc.contributor.author	Dellaert, Frank
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Interactive Computing	en_US
dc.contributor.corporatename	Carnegie-Mellon University. Robotics Institute	en_US
dc.contributor.corporatename	Technion - Israel Institute of Technology	en_US
dc.contributor.corporatename	Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel	en_US
dc.date.accessioned	2015-08-05T15:08:20Z
dc.date.available	2015-08-05T15:08:20Z
dc.date.issued	2015-05
dc.description	© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.	en_US
dc.description	DOI: 10.1109/ICRA.2015.7140012
dc.description.abstract	We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment sing indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.	en_US
dc.embargo.terms	null	en_US
dc.identifier.citation	Dong, Jing; Nelson, Erik; Indelman, Vadim; Michael, Nathan; & Dellaert, Frank (2015). "Distributed Real-time Cooperative Localization and Mapping Using an Uncertainty-aware Expectation Maximization Approach". Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2015), 26-30 May 2015, pp. 5807-5814.	en_US
dc.identifier.doi	10.1109/ICRA.2015.7140012
dc.identifier.uri	http://hdl.handle.net/1853/53707
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Cooperative localization and mapping	en_US
dc.subject	Distributed cooperative mapping	en_US
dc.subject	Expectation maximization	en_US
dc.subject	Loop closures	en_US
dc.subject	Multi-robot	en_US
dc.title	Distributed Real-time Cooperative Localization and Mapping Using an Uncertainty-Aware Expectation Maximization Approach	en_US
dc.type	Text
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Dellaert, Frank
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication	dac80074-d9d8-4358-b6eb-397d95bdc868
relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf