Vision-Based Vineyard Navigation Solution with Automatic Annotation

Autonomous navigation is crucial for achieving the full automation of agricultural research and production management using agricultural robots. In this paper, we present a vision-based autonomous navigation approach for agriculture robots in trellised cropping systems, which stands out for its remarkable performance achieved entirely without human annotation. We propose a novel learning-based method that directly estimates the path traversibility heatmap from an RGB-D image and subsequently converts it into a preferred traversal path. One key advantage of our approach lies in its capability to predict the robot's preferred path directly, allowing us to obtain training labels without manual annotation. Specifically, we propose an automatic annotation pipeline that leverages the robot's path recorded during data collection. Furthermore, we develop a full navigation framework by integrating our path detection model with row switching modules, enabling the robot to smoothly transition between crop rows within the vineyard. We conduct extensive field trials in three different vineyards to validate the performance of our autonomous navigation framework. The results demonstrate that our approach provides a cost-effective, accurate, and robust solution for vineyard navigation.

Authors

• Ertai Liu
• Josephine Monica
• Kaitlin M. Gold
• Lance Cadle-Davidson
• David Combs
• Yu Jiang

Keywords

• Heating systems
• Training
• Production management
• Annotations
• Navigation
• Pipelines
• Switches
• Automatic Annotation
• Navigation Solution
• Manual Annotation
• Cropping Systems
• Training Labels
• Autonomous Navigation
• Preferential Paths
• RGB-D Images
• Crop Row
• Training Data
• Deep Learning
• Convolutional Layers
• Human Intervention
• Crop Plants
• Point Cloud
• Robotic System
• Image Space
• Inertial Measurement Unit
• Navigation System
• Depth Camera
• End Of Row
• Front Camera
• Representative View
• Trained Data Collectors
• Robot Path
• Prominent Structures
• Row Planting
• Bird’s Eye
• Field Of Robotics
• Depth Information