Robust Visual Servoing with Adaptive Kalman Filtering for Dynamic Environments

Visual servoing techniques offer a compelling approach to robot control by directly utilizing visual information for feedback. However, performance degrades significantly in dynamic environments characterized by unpredictable object motion and occlusions. This paper presents a robust visual servoing framework incorporating an adaptive Kalman filter to estimate object pose and velocity, mitigating the impact of noisy sensor data and dynamic disturbances. The proposed approach dynamically adjusts the filter's process and measurement noise covariance matrices based on real-time observations, enhancing tracking accuracy and control stability. Experimental results demonstrate the efficacy of the proposed framework in a simulated robotic manipulation task involving a moving target object.