  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.
