Impact of Localization Errors on Label Quality for Online HD Map Construction
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,...
| Authors | Alexander Blumberg, Jonas Merkert, Richard Fehler et al. |
|---|---|
| Primary category | cs.RO |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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