NDLI: A distributed multi robot SLAM system for environment learning

Content Provider	IEEE Xplore Digital Library
Author	Jafri, S.R.U.N. Chellali, R.
Copyright Year	2013
Description	Author affiliation: Pattern Anal. & Comput. Vision (PAVIS) Lab., Univ. degli Studi di Genova, Genoa, Italy (Jafri, S.R.U.N.) \|\| Pattern Anal. & Comput. Vision (PAVIS) Lab., Ist. Italiano di Tecnol., Genoa, Italy (Chellali, R.)
Abstract	This paper presents a multi mobile robot simultaneous localization and mapping (SLAM) system for feature based environment learning by using team of exploring robots. The environmental information is measured through the dynamic sensor network in the shape of moving robots with unknown initial poses. Each robot is equipped with 2D laser scanner and a webcam and it serves as a moving sensor node to perceive horizontal and vertical line features respectively. All the moving nodes are responsible to build the informational structured space. The proposed system is using a unified Extended Kalman Filter (EKF) based SLAM framework for each robot which eventually builds a line feature based partial 3D model of the environment. Each moving robotic sensor node then shares its feature based map model to other moving nodes which are in communication range. A global map model is then transformed after getting mutual pose estimation of the robots by matching mutual common map features in addition by taking visual confirmation of other robot. The proposed system has been tested in an indoor environment and results are shown in the paper.
Starting Page	82
Ending Page	88
File Size	1439478
Page Count	7
File Format	PDF
ISBN	9781467358774
DOI	10.1109/RiiSS.2013.6607933
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping EKF SLAM Lasers Robot vision systems scan matching Cameras Feature extraction MonoSLAM
Content Type	Text
Resource Type	Article

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