What Really Matters for Robust Multi-Sensor HD Map Construction?
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
High-definition (HD) map construction methods are crucial for providing
precise and comprehensive static environmental information, which is essential
for autonomous driving systems. While Camera-LiDAR fusion techniques have shown
promising results by integrating data from both modalities, existing approaches
primarily focus on improving model accuracy and often neglect the robustness of
perception models, which is a critical aspect for real-world applications. In
this paper, we explore strategies to enhance the robustness of multi-modal
fusion methods for HD map construction while maintaining high accuracy. We
propose three key components: data augmentation, a novel multi-modal fusion
module, and a modality dropout training strategy. These components are
evaluated on a challenging dataset containing 10 days of NuScenes data. Our
experimental results demonstrate that our proposed methods significantly
enhance the robustness of baseline methods. Furthermore, our approach achieves
state-of-the-art performance on the clean validation set of the NuScenes
dataset. Our findings provide valuable insights for developing more robust and
reliable HD map construction models, advancing their applicability in
real-world autonomous driving scenarios. Project website:
https://robomap-123.github.io.
