A sensor fusion approach for improving implementation speed and accuracy of RTAB-Map algorithm based indoor 3D mapping

In recent years, 3D mapping for indoor environments has undergone considerable research and improvement because of its effective applications in various fields, including robotics, autonomous navigation, and virtual reality. Building an accurate 3D map for indoor environment is challenging due to the complex nature of the indoor space, the problem of real-time embedding and positioning errors of the robot system. This study proposes a method to improve the accuracy, speed, and quality of 3D indoor mapping by fusing data from the Inertial Measurement System (IMU) of the Intel Realsense D435i camera, the Ultrasonic-based Indoor Positioning System (IPS), and the encoder of the robot's wheel using the extended Kalman filter (EKF) algorithm. The merged data is processed using a Real-time Image Based Mapping algorithm (RTAB-Map), with the processing frequency updated in synch with the position frequency of the IPS device. The results suggest that fusing IMU and IPS data significantly improves the accuracy, mapping time, and quality of 3D maps. Our study highlights the proposed method's potential to improve indoor mapping in various fields, indicating that the fusion of multiple data sources can be a valuable tool in creating high-quality 3D indoor maps.