NDLI: Large scale mapping using submaps

Content Provider	IEEE Xplore Digital Library
Author	Lupea, D. Majdik, A. Lazea, G.
Copyright Year	2012
Description	Author affiliation: Department of Automation, Technical University of Cluj-Napoca, Romania (Lupea, D.; Majdik, A.; Lazea, G.)
Abstract	The main contribution of this article the implementation of a new method for the usage of submaping algorithms, which surpasses the limitations arising from the need of statistical independence between the submaps, when mapping large scale areas. This method relies on the commonly used structure of SLAM (Simultaneous Localization and Mapping) mandatory for building submaps which share information, but stay conditionally independent. This approach is able to recover the final map, without introducing other approximations ignoring the ones already introduced by the EKF (Extended Kalman Filter), all this in a linear time. The algorithm has been tested for the case of local submaps because the effects of linearization errors are smaller in this way. The obtained maps are more accurate and consistent compared with ones obtained by EKF or EIF (Extended Information Filter) methods, which are not using local coordinates.
Starting Page	412
Ending Page	416
File Size	267460
Page Count	5
File Format	PDF
ISBN	9781467307017
e-ISBN	9781467307048
DOI	10.1109/AQTR.2012.6237745
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-24
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping SLAM Detectors robotics Feature extraction Mathematical model submaps loop-closure Equations Vehicles
Content Type	Text
Resource Type	Article

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