The Good, the Sparse, and the Ugly: Investigating the Impact of Corrupted HD-Map Features on Ego-Vehicle Localization

This paper investigates the impact of a corrupted map on the quality of GNSS-free localization. Using a High Definition (HD) map, we gradually remove and shift features in space. From originally 200 features per kilometer, we randomly discard up to 90% of our map and introduce perturbations of up to +/-0.9m in each direction to our HD map features. Our evaluation is based on a GNSS-free LiDAR-SLAM, which utilizes panoptic segmentation to observe geometric primitives. In the back-end, a graph optimization is performed to estimate the vehicle’s and the landmarks’ positions. Further, we also assess the impact of conducting pure localization instead of Simultaneous Localization and Mapping (SLAM). The effects are evaluated using Mean Absolute Error for accuracy evaluation. For stability assessment, we calculate two percentiles: one for deviations of 0.3m or less and the second for deviations of 2m or less. We conduct real-life experiments with our testing vehicle which is equipped with a reference system based on RTK-GNSS. We use a commercial, standardized HD map of our suburban campus. Our findings aim to provide insights into the tradeoffs between mapping effort, map maintenance and accurate positioning. We demonstrate that a simultaneous mapping of the environment enhances the stability of localization.