Suitable for Dynamic Modeling of 3D Vision Algorithm and Motion Control Compensation

The accuracy of 3D modeling in dynamic scenes is constrained by motion blur and system latency. Traditional visual algorithms often produce geometric distortions when reconstructing high-speed moving objects, and the response delay in control loops further exacerbates these modeling errors. This paper introduces a collaborative framework that integrates time-varying perception 3D vision with predictive compensation control: first, a motion state estimation module based on multi-sensor tight coupling is designed. This module fuses RGB-D data and IMU information using adaptive Kalman filtering to achieve real-time decoupling of motion trajectories. Next, a hierarchical control compensation mechanism is developed, which combines feedforward motion prediction from LSTM networks with online tuning of PID parameters based on visual-inertial feedback. This significantly reduces modeling distortions caused by actuator delays. Verification on a robotic arm dynamic grasping platform shows that compared to the ORB-SLAM3 system, the modeling point cloud registration error is reduced by 62.3%, and the root mean square error (RMSE) of trajectory tracking is reduced by 58.1%. This effectively addresses the industry challenge of 'modeling-control' cross-interference in dynamic scenes, providing robust technical support for scenarios such as intelligent manufacturing and unmanned systems.