Factor Graph-Based Ground Truth Trajectory Estimation by Fusing Robotic Total Station and Inertial Measurements

The application of mobile mapping systems (MMS) has increased continuously in the last decades in fields like infrastructure or ecosystem monitoring. Equipped with multiple laser scanners and cameras, these systems can generate high-resolution 3D point clouds of the environment in a short time. In this process, the accuracy of the trajectory of the system is of central importance as it directly affects the accuracy of the resulting point cloud. However, since the trajectory estimation depends on sensor observations that are often affected by unknown systematic errors, the actual accuracy of the trajectory remains mainly unknown. To uncover the gap in the trajectory accuracy assessment, we present a method to create ground truth trajectories for mobile mapping systems by integrating millimeter-accurate total station measurements. We mount an Inertial Measurement Unit (IMU) and two 360-degree prisms on a mobile platform, track them with two Robotic Total Stations (RTS) during motion, and fuse these prism measurements with the IMU readings using a factor graph-based trajectory estimation approach. To evaluate the quality of this ground truth trajectory, we record repeated measurements on a closed-loop rail track close to Bonn, Germany. The results show that the generated ground truth trajectory estimated with RTS and IMU data achieves a precision of around 1 mm in position and 0.05$^\circ$ in orientation. To show the potential of the method, we detect systematic deviations of an example MSS that uses Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) and IMU data for trajectory estimation. The results show that even under good GNSS conditions, the ground truth trajectory from our proposed approach has significantly better precision and less systematic errors than the trajectory based on RTK-GNSS and IMU data.