Long-Period Localization Method for LiDAR Based on Local Mapping

The precise positioning of driverless vehicles on unstructured roads extensively relies on LiDAR-based simultaneous localization and mapping (SLAM). However, the problem of localization loss, caused by the failure of pre-built map matching due to environmental changes, has been an industry challenge and a popular research direction. To address the aforementioned problems, this study proposes a long-term robust localization method, online location normal distributions transform (OL-NDT), which uses LiDAR and inertial measurement units to combine real-time local map matching based on NDT localization. OL-NDT inputs the localization information obtained by NDT as measurement information factors into the factor map to optimize the local maps constructed in real time and uses real-time local maps for localization after NDT localization is lost. OL-NDT is tested on the MulRan dataset and achieves a cumulative error percentage of 0.40%, which is 1.06 percentage points lower than the existing traditional localization methods. This effectively improves localization accuracy and enables accurate localization in scenarios with significant changes in the static structure. Moreover, the campus data collected by Beijing Union University is used to verify that the localization trajectory accuracy of OL-NDT precisely matches the known map, even in cases of short-term missing maps.