用校园高尔夫车实测在线建图，解决动态环境地图更新难题
Online Mapping for Autonomous Driving: Addressing Sensor Generalization and Dynamic Map Updates in Campus Environments
- 用双摄像头+激光雷达实时采集数据，结合细调模型生成3D地图
- 在校园环境中实现高精度地图预测与持续增量更新
- 适合关注真实场景自动驾驶地图构建的开发者与研究者
高精地图对自动驾驶至关重要，可提供道路边界、车道线和人行横道等精确信息以保障安全导航。然而传统高精地图生成成本高、耗时长，且难以在动态环境中维护。为此，我们在配备双前摄相机和激光雷达的校园高尔夫车平台上部署了在线建图系统，解决了三大核心挑战：(1) 校园环境3D高精地图标注；(2) 在车载端集成并泛化SemVecMap模型；(3) 逐步生成并更新预测高精地图以捕捉环境变化。通过使用校园特有数据微调，该流程实现了高精度地图预测，并支持持续更新，验证了其在真实自动驾驶场景中的实用价值。
原文摘要 · Abstract (English)
High-definition (HD) maps are essential for autonomous driving, providing precise information such as road boundaries, lane dividers, and crosswalks to enable safe and accurate navigation. However, traditional HD map generation is labor-intensive, expensive, and difficult to maintain in dynamic environments. To overcome these challenges, we present a real-world deployment of an online mapping system on a campus golf cart platform equipped with dual front cameras and a LiDAR sensor. Our work tackles three core challenges: (1) labeling a 3D HD map for campus environment; (2) integrating and generalizing the SemVecMap model onboard; and (3) incrementally generating and updating the predicted HD map to capture environmental changes. By fine-tuning with campus-specific data, our pipeline produces accurate map predictions and supports continual updates, demonstrating its practical value in real-world autonomous driving scenarios.
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