The objective of this study was to develop a visual navigation system capable of navigating an unmanned ground vehicle(UGV)travelling between tree rows in the outdoor orchard.Thus far,while most research has developed...The objective of this study was to develop a visual navigation system capable of navigating an unmanned ground vehicle(UGV)travelling between tree rows in the outdoor orchard.Thus far,while most research has developed algorithms that deal with ground structures in the orchard,this study focused on the background of canopy plus sky to eliminate the interference factors such as inconsistent lighting,shadows,and color similarities in features.Aiming at the problem that the traditional Hough transform and the least square method are difficult to be applied under outdoor conditions,an algorithm combining Hough matrix and random sample consensus(RANSAC)was proposed to extract the navigation path.In the image segmentation stage,this study used an H-component that was adopted to extract the target path of the canopy plus sky.Then,after denoising and smoothing the image by morphological operation,line scanning was used to determine the midpoint of the target path.For navigation path extraction,this study extracted the feature points through Hough matrix to eliminate the redundant points,and RANSAC was used to reduce the impact of the noise points caused by different canopy shapes and fit the navigation path.The path acquisition experiment proved that the accuracy of Hough matrix and RANSAC method was 90.36%-96.81%and the time consumption of the program was within 0.55 s under different sunlight intensities.This method was superior to the traditional Hough transform in real-time and accuracy,and had higher accuracy,slightly worse real-time compared with the least square method.Furthermore,the OPENMV was used to capture the ground information of the orchard.The experiment proved that the recognition rate of OPENMV for identifying turning information was 100%,and the program running time was 0.17-0.19 s.Field experiments showed that the UGV could autonomously navigate the rows with a maximum lateral error of 0.118 m and realize the automatic turning of the UGV.The algorithm satisfied the practical operation requirements of autonomous vehicles in the orchard.So the UGV has the potential to guide multipurpose agricultural vehicles in outdoor orchards in the future.展开更多
基金supported by the Special Fund for Agro-scientific Research in the Public Interest(Grant No.201503136)the National Key Technology R&D Program(Grant No.2017YFD0301300).
文摘The objective of this study was to develop a visual navigation system capable of navigating an unmanned ground vehicle(UGV)travelling between tree rows in the outdoor orchard.Thus far,while most research has developed algorithms that deal with ground structures in the orchard,this study focused on the background of canopy plus sky to eliminate the interference factors such as inconsistent lighting,shadows,and color similarities in features.Aiming at the problem that the traditional Hough transform and the least square method are difficult to be applied under outdoor conditions,an algorithm combining Hough matrix and random sample consensus(RANSAC)was proposed to extract the navigation path.In the image segmentation stage,this study used an H-component that was adopted to extract the target path of the canopy plus sky.Then,after denoising and smoothing the image by morphological operation,line scanning was used to determine the midpoint of the target path.For navigation path extraction,this study extracted the feature points through Hough matrix to eliminate the redundant points,and RANSAC was used to reduce the impact of the noise points caused by different canopy shapes and fit the navigation path.The path acquisition experiment proved that the accuracy of Hough matrix and RANSAC method was 90.36%-96.81%and the time consumption of the program was within 0.55 s under different sunlight intensities.This method was superior to the traditional Hough transform in real-time and accuracy,and had higher accuracy,slightly worse real-time compared with the least square method.Furthermore,the OPENMV was used to capture the ground information of the orchard.The experiment proved that the recognition rate of OPENMV for identifying turning information was 100%,and the program running time was 0.17-0.19 s.Field experiments showed that the UGV could autonomously navigate the rows with a maximum lateral error of 0.118 m and realize the automatic turning of the UGV.The algorithm satisfied the practical operation requirements of autonomous vehicles in the orchard.So the UGV has the potential to guide multipurpose agricultural vehicles in outdoor orchards in the future.
