Deep Learning-Based Real-Time 6D Pose Estimation and Multi-Mode Tracking Algorithms for Citrus-Harvesting Robots

Abstract

In the agricultural sector, utilizing robots for tasks such as fruit harvesting poses significant challenges, particularly in achieving accurate 6D pose estimation of the target objects, which is essential for precise and efficient harvesting. Particularly, fruit harvesting relies heavily on manual labor, leading to issues with an unstable labor supply and rising costs. To solve these problems, agricultural harvesting robots are gaining attention. However, effective harvesting necessitates accurate 6D pose estimation of the target object. This study proposes a method to enhance the performance of fruit-harvesting robots, including the development of a dataset named HWANGMOD, which was created using both virtual and real environments with tools such as Blender and BlenderProc. Additionally, we present methods for training an EfficientPose-based model for 6D pose estimation and ripeness classification, and an algorithm for determining the optimal harvest sequence among multiple fruits. Finally, we propose a multi-object tracking method using coordinates estimated by deep learning models to improve the robot’s performance in dynamic environments. The proposed methods were evaluated using metrics such as ADD and ADDS, showing that the deep learning model for agricultural harvesting robots excelled in accuracy, robustness, and real-time processing. These advancements contribute to the potential for commercialization of agricultural harvesting robots and the broader field of agricultural automation technology.