摘要
机器人代替人工完成旁路作业任务,需要其在复杂的作业场景下具有自主估计目标物体位姿的能力。针对旁路作业机器人在复杂背景和不同光照条件下目标位姿的实时估计问题,提出一种基于改进YOLO-6D且融合Transformer模型的6D位姿估计算法(RTFT6D),改进YOLOv8主干网络以提升推理速度,设计一种融合Transformer模型的特征加强网络,提升位姿估计的鲁棒性。实验结果表明,该算法在LINEMOD数据集上的精度超过了大多数基于RGB图像输入的位姿估计算法,并且针对不同光照条件下的配网旁路作业目标具有很好的位姿估计效果。
To replace humans in bypass operation tasks,robots need the ability to autonomously estimate the pose of target objects in complex work environments.Addressing the problem of real-time pose estimation of targets by bypass operation robots under complex backgrounds and varying lighting conditions,this proposes a 6D pose estimation algorithm(RTFT6D)based on improved YOLO6D integrated with Transformer model.The YOLOv8 backbone network is modified to enhance inference speed,and a feature enhancement network incorporating the Transformer model is designed to improve the robustness of pose estimation.The experimental results show that the proposed algorithm surpasses most RGB image-based pose estimation algorithms in accuracy on the LINEMOD dataset,and it achieves excellent pose estimation performance for bypass operation targets under different lighting conditions.
作者
姚杰
殷洪海
汪大海
李润梓
张茜雯
郭毓
YAO Jie;YIN Honghai;WANG Dahai;LI Runzi;ZHANG Qianwen;GUO Yu(Changzhou Power Supply Branch,State Grid Jiangsu Electric Power Co.Ltd.,Changzhou 213000,China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《计算机与现代化》
2025年第9期20-26,共7页
Computer and Modernization
基金
国家电网有限公司科技项目(J2023016)
国家自然科学基金面上项目(61973167)。
