Paper Info
Title
SLAM with salient line feature extraction in indoor environments
Abstract
This paper presents a simultaneous localization and mapping (SLAM) of a large indoor environment using Rao-Blackwellized particle filter (RBPF) along with line segments as the landmarks. To represent the environment as a compact form, we use only two end points of the line segment, reducing computational cost in modeling line uncertainty. With a modified scan point clustering method, the proposed adaptive iterative end point fitting (IEPF) plays an important role in estimating line parameters by taking a noisy scan point near end points into account. Thus, by line-segment matching the robot is localized well in a local frame. We also introduce an online global optimization of a map, which provides more consistent map by removing spurious lines and merging collinear lines. Each of our approaches is efficiently integrated into the proposed RBPF-SLAM framework. Experiments with well-known data set demonstrate that the proposed method provides a reliable SLAM performance along with a compact map representation.
Year
DOI
Venue
2010
10.1109/ICARCV.2010.5707254
ICARCV
Keywords
Field
DocType
particle filtering (numerical methods),rbpf,salient line feature extraction,computational cost,slam,mapping,iepf,collinear lines,feature extraction,localization,slam (robots),iterative end point fitting,iterative end point fitting (iepf),indoor environments,iterative methods,rao-blackwellized particle filter,line segment,global optimization,simultaneous localization and mapping,covariance matrix,clustering algorithms
Computer vision,Line segment,Global optimization,Iterative method,Computer science,Particle filter,Feature extraction,Artificial intelligence,Covariance matrix,Simultaneous localization and mapping,Cluster analysis
Conference
ISSN
ISBN
Citations 
2474-2953
978-1-4244-7814-9
5
PageRank 
References 
Authors
0.45
9
4
Name
Order
Citations
PageRank
Su-Yong An1687.38
Jeong-Gwan Kang2575.39
Lae-Kyoung Lee3253.33
Se-Young Oh444263.23