Position Correction and Coordinate System Fusion for Multi-Robot Arm Systems Using Multiple LiDAR Sensors

This paper addresses the challenge of improving the accuracy and robustness of multi-robotic arm coordinate system fusion and position correction using multiple LiDAR sensors. We propose an enhanced adaptive Extended Kalman Filter (A-EKF) that dynamically adjusts process and measurement noise covariances based on real-time sensor data and environmental conditions. The theoretical framework encompasses spatial coordinate transformation, sensor data integration, and error mitigation strategies, ensuring precise harmonization of multisensor data. Convergence analysis demonstrates the stability and effectiveness of the proposed A-EKF, and numerical experiments validate its superior performance over traditional EKF methods, particularly in scenarios with varying noise characteristics. The findings highlight the potential of the proposed method for applications requiring high-precision positioning in dynamic and complex environments.