A Sensor Fusion Approach for Improving Implementation Speed and Accuracy of RTAB-Map Algorithm Based Indoor 3D Mapping

Due to its numerous successful applications in industries like robotics, and autonomous navigation, 3D mapping for indoor environments has undergone much research and development. The complexity of the environment, a real-time embedding issue, and positioning mistakes of the robot system affect the creation of an accurate 3D map of indoor. Our research proposes a method to improve the 3D map construction performance by fusing data from the Ultrasonic-based Indoor Positioning System (IPS), the Inertial Measurement System (IMU) of the Intel Realsense D435i camera, and the encoder of the robot's wheel using the extended Kalman filter (EKF) algorithm. A Real-time Image Based Mapping algorithm (RTAB-Map) is used to handle the combined data, with the processing frequency updated in time with the IPS device's position frequency. The results indicate that combining sensors data considerably increases the speed, accuracy, and quality of the 3D mapping process. Our research demonstrates the potential of the integration of diverse data sources may be a useful tool for producing high-standard 3D indoor maps. © 2023 IEEE.