NDLI: Uncalibrated monocular based simultaneous localization and mapping for indoor autonomous mobile robot navigation

Content Provider	IEEE Xplore Digital Library
Author	Siyao Fu Guosheng Yang
Copyright Year	2009
Description	Author affiliation: Central University of Nationalities, Beijing, 100081, China (Siyao Fu; Guosheng Yang)
Abstract	This paper describes a an SLAM algorithm for the navigation for an indoor autonomous mobile robot. The main emphasis of this paper is on the ability of line extraction. A recognition method based on straight line extraction is proposed for extracting the key features on the office ceiling, in an effort to estimate the pose of mobile robot. Random Sample Consensus (RANSAC) paradigm is used to group the line segments. During the navigation, onboard odometry is used at the beginning stage to estimate the information of environment for visual reckoning, while lamps on the ceiling act as beacons for positioning to eliminate accumulation of errors after a long-term run. The data captured from infrared sensors is used for constructing a map. The proposed method scales well with respect to the size of the input image and the number and size of the shapes within the data. Moreover the algorithm is conceptually simple and easy to implement. Simulation and experimental results show that good recognition and localization can be achieved using the proposed method, allowing for the interested region correspondence matching and mapping between images from different sensors or the same sensor indifferent time phrase.
Starting Page	663
Ending Page	668
File Size	1509184
Page Count	6
File Format	PDF
ISBN	9781424434916
DOI	10.1109/ICNSC.2009.4919356
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-26
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping Mobile robots Navigation Data mining Image sensors Feature extraction Lamps Infrared sensors Shape Image recognition
Content Type	Text
Resource Type	Article

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