用双视角视觉实现机器人抓取中的姿态与速度协同估计
Correlation-Aware Dual-View Pose and Velocity Estimation for Dynamic Robotic Manipulation
- 基于李群的分布式滤波器，分别处理眼在手上和眼到手视角数据
- 在真实机械臂上实验，相比前沿方法误差降低23%以上
- 适合动态目标抓取、对传感器延迟敏感的场景
准确的姿态与速度估计对机器人操作的空间任务规划至关重要。传统集中式传感器融合虽有效，本文提出一种新型分布式融合方法，同时估计姿态与速度。通过安装在机械臂上的眼在手上与眼到手双视角视觉配置，追踪一个服从随机游走运动模型（随机加速度模型）的目标物体。机器人运行两个独立的自适应扩展卡尔曼滤波器，构建于矩阵李群之上，分别在 $\mathbb{SE}(3) \times \mathbb{R}^3 \times \mathbb{R}^3$ 上预测状态，在 $\mathbb{SE}(3)$ 上更新。最终融合状态通过李群上的相关性感知融合规则获得。该方法在配备Intel RealSense相机的UFactory xArm 850上进行测试，跟踪移动目标。实验结果验证了所提去中心化双视角估计框架的有效性与鲁棒性，在多个指标上持续优于现有先进方法。
原文摘要 · Abstract (English)
Accurate pose and velocity estimation is essential for effective spatial task planning in robotic manipulators. While centralized sensor fusion has traditionally been used to improve pose estimation accuracy, this paper presents a novel decentralized fusion approach to estimate both pose and velocity. We use dual-view measurements from an eye-in-hand and an eye-to-hand vision sensor configuration mounted on a manipulator to track a target object whose motion is modeled as random walk (stochastic acceleration model). The robot runs two independent adaptive extended Kalman filters formulated on a matrix Lie group, developed as part of this work. These filters predict poses and velocities on the manifold $\mathbb{SE}(3) \times \mathbb{R}^3 \times \mathbb{R}^3$ and update the state on the manifold $\mathbb{SE}(3)$. The final fused state comprising the fused pose and velocities of the target is obtained using a correlation-aware fusion rule on Lie groups. The proposed method is evaluated on a UFactory xArm 850 equipped with Intel RealSense cameras, tracking a moving target. Experimental results validate the effectiveness and robustness of the proposed decentralized dual-view estimation framework, showing consistent improvements over state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。