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实时6D姿势估计和多目标跟踪,用于低成本多机器人系统

Bo Shan¹, Donghui Zhao^1,2, Ruijin Zhao¹

¹School of Electrical Engineering, Shenyang University of Technology, Shenyang 110178, China.

Sensors (Basel, Switzerland)

|December 11, 2025

概括

本研究介绍了YolPnP-FT,这是一个低成本的实时运动捕捉系统,用于多机器人合作. 它可以使用单个深度摄像头进行准确的6D姿势估计和跟踪,这对于系统验证至关重要.

科学领域:

机器人技术机器人技术机器人技术
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

可靠和负担得起的运动捕捉对于开发和验证多机器人合作系统至关重要.
传统的运动捕捉系统往往过于昂贵,限制了研究和开发的可访问性.

研究的目的:

为多机器人系统开发低成本,实时6D姿势估计和跟踪方法.
使用单个深度摄像头实现同时进行机器人分类,姿势估计和多目标跟踪.

主要方法:

拟议的YolPnP-FT管道将YOLOv8检测与关键点信任选策略 (PnP-FT) 整合在一起.
用高斯惩罚软NMS来提高对部分遮的强度.
马哈拉诺比斯和等号距离的组合确保了对视觉上相似的机器人稳定的ID分配.

主要成果:

该系统在摄像头高度低于2.5米时,平均位置误差低于0.009米,平均角度误差低于4.2°.
在1920 × 1080分辨率下保持了19.8 FPS的稳定跟踪率.
感知输出在CoppeliaSim模拟中得到了验证,证实了它们对协调任务的有用性.

结论:

YolPnP-FT方法为多机器人系统提供了可部署,低成本和实时感知解决方案.
这种方法显著降低了在多机器人研究中捕捉运动的成本障碍.
该系统的准确性和稳定性使其适用于现实应用和下游协调任务.

关键词:

6D姿势估计估计在RGB传感器上,RGB传感器可以传感RGB.低成本的看法是低成本的多机器人系统多机器人系统多目标追踪系统多目标追踪系统实时感知真实时间感知

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