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http://dx.doi.org/10.9708/jksci.2019.24.11.029

Vision-based Real-time Lane Detection and Tracking for Mobile Robots in a Constrained Track Environment  

Kim, Young-Ju (Division of Computer Software Engineering, Silla University)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.11, 2019 , pp. 29-39 More about this Journal
Abstract
As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.
Keywords
Autonomous Driving; Lane Detection; Lane Tracking; Kalman Filter; ROS;
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