Abstract:Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle's self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

No abstract