Impact of Localization Errors on Label Quality for Online HD Map Construction

2026-03-03

AI Summary

This study addresses the susceptibility of online high-definition map construction to localization errors when using prior maps as supervisory labels, which can lead to label distortion. By introducing three types of synthetic localization noise—Ramp, Gaussian, and Perlin—into the Argoverse 2 dataset, the authors train MapTRv2 variants to systematically quantify, for the first time, the impact of different localization errors on label quality. The findings reveal that heading angle errors are more detrimental than positional errors, with their adverse effects magnified at greater distances. To better align evaluation with real-world driving requirements, a distance-weighted metric is proposed. Experiments demonstrate that model performance degrades superlinearly with increasing noise levels, while access to undistorted ground-truth data significantly enhances performance.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

High-definition (HD) maps are crucial for autonomous vehicles, but their creation and maintenance is very costly. This motivates the idea of online HD map construction. To provide a continuous large-scale stream of training data, existing HD maps can be used as labels for onboard sensor data from consumer vehicle fleets. However, compared to current, well curated HD map perception datasets, this fleet data suffers from localization errors, resulting in distorted map labels. We introduce three kinds of localization errors, Ramp, Gaussian, and Perlin noise, to examine their influence on generated map labels. We train a variant of MapTRv2, a state-of-the-art online HD map construction model, on the Argoverse 2 dataset with various levels of localization errors and assess the degradation of model performance. Since localization errors affect distant labels more severely, but are also less significant to driving performance, we introduce a distance-based map construction metric. Our experiments reveal that localization noise affects the model performance significantly. We demonstrate that errors in heading angle exert a more substantial influence than position errors, as angle errors result in a greater distortion of labels as distance to the vehicle increases. Furthermore, we can demonstrate that the model benefits from non-distorted ground truth (GT) data and that the performance decreases more than linearly with the increase in noisy data. Our study additionally provides a qualitative evaluation of the extent to which localization errors influence the construction of HD maps.

Problem

• localization errors
• HD map construction
• label quality
• autonomous driving
• map distortion

Innovation

• localization errors
• online HD map construction
• MapTRv2
• label distortion
• distance-based metric