The mobile mapping system (MMS) could become the foundation of digital twins and 3D modeling, and is widely applicable in a variety of fields, such as infrastructure management, intelligent transportation systems, and smart cities. However, data collected by MMS is extensive and complex, making data processing difficult. We present a novel method for segmenting urban assets (specifically in this case study traffic signs) with a lower-cost Azure Kinect and automatic data processing workflows. First, it was necessary to verify the reliability of this approach using the Time of Flight (ToF) camera from Azure Kinect to detect road signs outdoors. Using the data generated by the ToF camera, we then extracted the Region of Interest (ROI) quickly and efficiently. After transforming the ROI to the RGB image, we obtained the traffic sign area through a hybrid color-shape based method. In addition, we calculated the distance between the traffic sign and Azure Kinect based on the depth image. The Coefficient of Variation cv averaged 1.1%. It is thus evident that Azure Kinect is reliable for outdoor traffic sign segmentation. Our algorithm has been compared with deep learning algorithms. According to our analysis, our algorithm has an accuracy of 0.8216, while the accuracy of deep learning is 0.7466, which indicates that our solution is more flexible and cost-effective.
Keywords: Mobile mapping system (MMS), Azure Kinect,