Off-Road Lane Detection Using Superpixel Clustering and RANSAC Curve Fitting
- Resource Type
- Conference
- Authors
- Agrawal, Sanskar; Deo, Indu Kant; Haldar, Siddhant; Kranti Kiran, G Rahul; Lodhi, Vaibhav; Chakravarty, Debashish
- Source
- 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Control, Automation, Robotics and Vision (ICARCV), 2018 15th International Conference on. :1942-1946 Nov, 2018
- Subject
- Robotics and Control Systems
Cameras
Estimation
Image color analysis
Clustering algorithms
Computational modeling
Land vehicles
Detection algorithms
- Language
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.