Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11772
Title: Sensor fusion of monocular cameras and laser rangefinders for line-based simultaneous localization and mapping (SLAM) tasks in autonomous mobile robots
Authors: Zhang, X
Rad, AB
Wong, YK
Keywords: Feature fusion
Multi-sensor point estimation fusion (MPEF)
Homography transform matrix
SLAM
Issue Date: 2012
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Sensors, 2012, v. 12, no. 1, p. 429-452 How to cite?
Journal: Sensors 
Abstract: This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.
URI: http://hdl.handle.net/10397/11772
EISSN: 1424-8220
DOI: 10.3390/s120100429
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

19
Last Week
0
Last month
0
Citations as of Sep 23, 2017

WEB OF SCIENCETM
Citations

16
Last Week
0
Last month
1
Citations as of Sep 21, 2017

Page view(s)

37
Last Week
0
Last month
Checked on Sep 24, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.