A Vision-based Navigation System for an Agricultural Autonomous Tractor

Abstract

RTK-GNSS (Real-time Kinematic-Global Navigation Satellite System) signal is necessary for the safe navigation of an autonomous tractor. However, the main problem associated with this type of precise GNSS is that the RTK correction signal might not be available at all geographical locations. Tree canopies present close to the tractors’ vicinity obstruct satellite signals and degrade the position accuracy. This can lead the autonomous tractor to travel outside of its predetermined path, which is unsafe. To avoid such situations, this study aims to develop a vision-based road detection system, which does not rely on RTK-GNSS. This system aims to detect both paved and unpaved roads in rural farm areas. It takes the RGB images obtained from an HD onboard camera and segments the road surface and road edges. The segmentation is performed by using several image filters, the sliding window method, and a set of rules that are determined manually. Finally, the lateral error obtained from the machine vision system is transmitted to the tractor's automatic navigation system. Experimental runs performed at the experimental farm of Hokkaido University showed that the lateral error calculated by this system is less than 0.2 m for unpaved roads and 0.4 m for paved roads. This shows that the system can detect both paved and unpaved roads. Future work addresses considerations to make the system more robust.