一种基于VideoPose5CH模型的潜水员水下6-DoF位置和定向估计方法
Kaidong Wang1,2, Yi Yang1,2, Qingbo Wei1,2
1State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了一种新方法,用于自动水下车辆 (AUV) 准确追踪潜水员的位置和方向. 视频Pose5CH网络增强了水下人类姿势的估计,改善了人机协作.
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
- 人与机器人的交互
背景情况:
- 准确的潜水者姿势估计对于人机协作中的自动水下车辆 (AUV) 来说至关重要.
- 现有的方法与水下潜水员的不刚性和可变姿势作斗争.
- 传统的深度学习和Perspective-n-Point (PnP) 算法通常是为刚性对象设计的.
研究的目的:
- 开发一个可靠的框架来估计潜水员的六度自由度 (6-DoF) 位置和方向.
- 引入一个新的神经网络架构,VideoPose5CH,用于水下人类姿势估计.
- 为了应对有限的水下3D人体姿势数据集的挑战.
主要方法:
- 提出了一个框架,利用2D联合坐标的时间序列作为视频Pose5CH网络的输入.
- 视频Pose5CH输出精细的3D和2D联合坐标,然后使用PNP算法进行6-DoF姿势恢复.
- 开发了适应水下条件的陆地3D人体姿势数据集增强策略.
主要成果:
- 提出的方法在2.643米至11.477米的范围内实现了潜水员6DoF位置和方向的稳定估计.
- 平均位置误差为7.33厘米 (X),4.04厘米 (Y) 和27.15厘米 (Z).
- 平均定位误差为6.96° (滚动),8.47° (俯冲) 和2.62° ().
结论:
- 新的VideoPose5CH网络和数据增强策略显著提高了不同位置估计的准确性和稳定性.
- 该框架允许在水下环境中可靠地跟踪潜水员的6DoF姿势和定位.
- 这一进步对于提高水下人机协作安全性和效率至关重要.
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