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.

翻译：近年来，室内环境三维地图构建因其在机器人技术、自主导航和虚拟现实等多个领域的有效应用而经历了大量的研究与改进。由于室内空间的复杂性、实时嵌入问题以及机器人系统的定位误差，构建精确的室内环境三维地图颇具挑战。本研究提出一种方法，通过融合Intel Realsense D435i摄像头的惯性测量系统（IMU）、基于超声波的室内定位系统（IPS）以及机器人轮式编码器的数据，并采用扩展卡尔曼滤波（EKF）算法，来提升室内三维地图构建的精度、速度与质量。合并后的数据使用实时图像建图算法（RTAB-Map）进行处理，其处理频率与IPS设备的位姿频率同步更新。结果表明，融合IMU与IPS数据显著提升了三维地图的精度、建图时间与质量。我们的研究凸显了所提方法在多个领域改善室内地图构建的潜力，表明多源数据融合可成为创建高质量室内三维地图的有力工具。