Factor Graph-Based Ground Truth Trajectory Estimation by Fusing Robotic Total Station and Inertial Measurements
Manuel Mittelstedt, Felix Esser, Gereon Tombrink, Lasse Klingbeil, Heiner kuhlmann
AI summary
Problem
The accuracy of mobile mapping system trajectories is often unknown due to sensor noise and systematic errors, while existing ground truth methods are either low-accuracy, limited to small scales, or require complex multi-station setups.
Approach
The method integrates millimeter-accurate position measurements from two robotic total stations tracking 360-degree prisms with high-frequency IMU data using a factor graph optimization framework to estimate a precise ground truth trajectory.
Key results
- Achieves ~1 mm position and 0.05° orientation precision on a closed-loop rail track
- Validates a factor graph noise model for RTS and IMU sensor fusion
- Detects systematic deviations in a state-of-the-art RTK-GNSS/IMU trajectory
- Demonstrates millimeter-accurate ground truth generation with only two total stations
Why it matters
Provides a reliable, high-precision benchmark for evaluating and improving trajectory estimation in mobile mapping systems and robotics.
Abstract
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 esti- mation 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◦ 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.