Dual 2D-RPLIDAR A1 Implementation for Simultaneous Object Scanning and Dynamic Mapping Using SLAM with Kalman Filter Integration

This study presents the development of a Dual 2D-RPLIDAR A1 system for simultaneous object scanning and dynamic mapping, integrating Hector SLAM and a Kalman Filter for enhanced accuracy and motion tracking. Calibration results showed that the dual LiDAR setup achieved measurement consistency across angles, with percentage errors reduced to as low as $0-2.9 \%$, confirming accurate sensor alignment. Distance measurements improved with the integration of the Kalman Filter, reducing the error by up to $4.4 \%$ compared to raw Euclidean distances. Motion tracking tests further validated system performance, with LiDAR-derived displacement, velocity, and acceleration data effectively capturing both subtle and abrupt object movements. Statistical analysis revealed mean displacement values of approximately $\mathbf{0 . 2}$ meters and acceleration variations reaching $\pm 22 \mathrm{~m} / \mathrm{s}^{2}$, demonstrating the system’s responsiveness and reliability in dynamic environments. The motion tracking subsystem maintained a real-time update rate of 7.15 Hz, ensuring low latency and smooth tracking of moving objects, while dynamic map structure through Hector SLAM operated at an average update rate of 0.51 Hz, suitable for static monitoring but limited for rapid environmental changes. Overall, the system achieved reliable real-time object tracking and dynamic mapping performance, with integrated SLAM and Kalman filtering contributing to improved detection accuracy, noise reduction, and enhanced environmental representation.