Title
Off-Road Lane Detection Using Superpixel Clustering And Ransac Curve Fitting
Abstract
Lane detection is the most important issue to be resolved for successful locomotion of Intelligent Ground Vehicles (IGV). Problems in lane detection often occur in an external setting mainly due to glare or shadow defects. A robust and real-time approach to off-road lane marker detection for IGVs is being presented here. A novel model fitting based lane detection algorithm has been developed. Linear combination of image planes is used which removes the background and uncovers the white lanes. Simple Linear Iterative Clustering is applied to the processed frame and essential thresholding is performed for noise reduction. Two operations namely a novel approach for lane model identification and estimation of chosen lane mode using RANSAC are followed in sequence on the obtained image. The proposed image processing pipeline has been successfully validated in outdoor field conditions.
Year
DOI
Venue
2018
10.1109/ICARCV.2018.8581155
2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV)
Field
DocType
ISSN
Noise reduction,Linear combination,Computer vision,Curve fitting,RANSAC,Computer science,Control theory,Image processing,Artificial intelligence,Thresholding,Cluster analysis,System identification
Conference
2474-2953
Citations 
PageRank 
References 
0
0.34
0
Authors
6