NDLI: Symmetrical model based SLAM [M-SLAM] for a quick map-search

Content Provider	IEEE Xplore Digital Library
Author	Jung-Suk Oh Kwee-Bo Sim
Copyright Year	2010
Description	Author affiliation: School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea (Jung-Suk Oh; Kwee-Bo Sim)
Abstract	The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm (called M-SLAM).
Starting Page	1993
Ending Page	1996
File Size	878301
Page Count	4
File Format	PDF
ISBN	9781424474530
e-ISBN	9788993215021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-27
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	IEEE International Conference on Control & Automation
Subject Keyword	Mobile Robot Simultaneous localization and mapping SLAM Symmetrical Model Feature extraction Prediction algorithms Data mining Mobile robots Sensor
Content Type	Text
Resource Type	Article

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