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

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