提升多传感器高清地图构建的鲁棒性，让自动驾驶更可靠。
What Really Matters for Robust Multi-Sensor HD Map Construction?
- 用数据增强、新融合模块和模态丢弃训练提升抗干扰能力
- 在NuScenes数据集上显著增强基线方法的鲁棒性
- 适合关注真实场景稳定性的自动驾驶地图研究者
高精度（HD）地图构建对提供精确完整的静态环境信息至关重要，是自动驾驶系统的核心。尽管相机-激光雷达融合技术通过整合多模态数据展现了良好效果，但现有方法主要关注模型精度，常忽视感知模型在真实应用中的鲁棒性。本文探索提升多模态融合方法在HD地图构建中的鲁棒性，同时保持高精度。提出三个关键组件：数据增强、新型多模态融合模块和模态丢弃训练策略。在包含10天NuScenes数据的挑战性数据集上评估，实验结果表明所提方法显著提升基线模型的鲁棒性。此外，该方法在NuScenes数据集干净验证集上达到当前最优性能。研究为开发更鲁棒可靠的HD地图构建模型提供了重要参考，推动其在真实自动驾驶场景中的应用。
原文摘要 · Abstract (English)
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.
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