Spatio-Temporal Alignment and Evaluation of Asynchronous Localization Systems in Real-World Urban Environments

Highly accurate, robust, and continuous localization is essential for the safe navigation and control of autonomous vehicles. Modern autonomous systems, including ROS 2-based platforms, rely on heterogeneous localization methods operating at different sampling rates and with different timing characteristics. Ignoring their temporal differences can lead to misleading trajectory error statistics. This paper presents an ROS 2-based localization evaluation framework that applies the spatio-temporal alignment of Tombrink et al. to asynchronous RTK-GNSS and LiDAR-based NDT trajectories using a sliding-window strategy. The framework was evaluated on a 767 s, approximately 3.67 km urban vehicle recording. In a same-solver comparison using samples not included in the preceding fitting windows, temporal compensation reduced horizontal ATE RMSE from 1.207 m to 0.740 m (38.7%). The results also distinguish local fitting errors from fixed-transform trajectory agreement and show that the estimated time shift depends on the calibration interval. The framework thus supports a documented comparison of asynchronously acquired localization trajectories under the evaluated conditions.