Visual servoing is fundamental to robotic applications, enabling precise positioning and control. However, applying it to textureless objects remains a challenge due to the absence of reliable visual features. Moreover, adverse visual conditions, such as occlusions, often corrupt visual feedback, leading to reduced accuracy and instability in visual servoing. In this work, we build upon learning-based keypoint detection for textureless objects and propose a method that enhances robustness by tightly integrating perception and control in a closed loop. Specifically, we employ an Extended Kalman Filter (EKF) that integrates per-frame keypoint measurements to estimate 6D object pose, which drives pose-based visual servoing (PBVS) for control. The resulting camera motion, in turn, enhances the tracking of subsequent keypoints, effectively closing the perception-control loop. Additionally, unlike standard PBVS, we propose a probabilistic control law that computes both camera velocity and its associated uncertainty, enabling uncertainty-aware control for safe and reliable operation. We validate our approach on real-world robotic platforms using quantitative metrics and grasping experiments, demonstrating that our method outperforms traditional visual servoing techniques in both accuracy and practical application.

翻译：视觉伺服是机器人应用的基础，能够实现精确定位与控制。然而，由于缺乏可靠的视觉特征，将其应用于无纹理物体仍具挑战性。此外，遮挡等不利视觉条件常会破坏视觉反馈，导致视觉伺服的精度降低与稳定性下降。本研究基于无纹理物体的学习式关键点检测方法，提出一种通过将感知与控制紧密集成于闭环中以增强鲁棒性的方法。具体而言，我们采用扩展卡尔曼滤波器（EKF）整合逐帧关键点测量值以估计物体六维位姿，进而驱动基于位姿的视觉伺服（PBVS）进行控制。由此产生的相机运动反过来增强了后续关键点的跟踪效果，有效形成了感知-控制闭环。此外，与标准PBVS不同，我们提出一种概率控制律，可同时计算相机速度及其关联的不确定性，从而实现不确定性感知控制以确保安全可靠运行。我们在真实机器人平台上通过定量指标与抓取实验验证了所提方法，结果表明本方法在精度与实际应用方面均优于传统视觉伺服技术。