无需GPS也能精确定位，用多模态地图匹配提升自动驾驶车辆在恶劣环境下的定位鲁棒性。
BEVMAPMATCH: Multimodal BEV Neural Map Matching for Robust Re-Localization of Autonomous Vehicles
- 融合激光雷达与摄像头，生成多模态鸟瞰图分割结果
- 通过交叉注意力搜索匹配候选地图块，实现厘米级定位精度
- 在无信号环境下召回率超39%，性能接近现有方法两倍
在无GNSS或弱GNSS环境下，自动驾驶车辆的定位面临严峻挑战。本文提出BEVMapMatch框架，无需依赖GNSS先验信息，在已知地图上实现鲁棒的车辆重定位。该方法采用上下文感知的激光雷达+摄像头融合策略，生成良好与恶劣天气条件下的多模态鸟瞰图（BEV）分割结果。基于交叉注意力机制的搜索算法，从地图中检索候选地图块用于匹配。最终以最优候选块对生成的BEV分割进行精细对齐，实现无需GNSS的精准全局定位。多帧BEV分割进一步提升定位精度。大量实验表明，该方法在无GNSS及恶劣环境下的重定位性能优于现有方法，召回率@1米达39.8%，近乎最佳基线的两倍。代码与数据将公开于https://github.com/ssuralcmu/BEVMapMatch.git。
原文摘要 · Abstract (English)
Localization in GNSS-denied and GNSS-degraded environments is a challenge for the safe widespread deployment of autonomous vehicles. Such GNSS-challenged environments require alternative methods for robust localization. In this work, we propose BEVMapMatch, a framework for robust vehicle re-localization on a known map without the need for GNSS priors. BEVMapMatch uses a context-aware lidar+camera fusion method to generate multimodal Bird's Eye View (BEV) segmentations around the ego vehicle in both good and adverse weather conditions. Leveraging a search mechanism based on cross-attention, the generated BEV segmentation maps are then used for the retrieval of candidate map patches for map-matching purposes. Finally, BEVMapMatch uses the top retrieved candidate for finer alignment against the generated BEV segmentation, achieving accurate global localization without the need for GNSS. Multiple frames of generated BEV segmentation further improve localization accuracy. Extensive evaluations show that BEVMapMatch outperforms existing methods for re-localization in GNSS-denied and adverse environments, with a Recall@1m of 39.8%, being nearly twice as much as the best performing re-localization baseline. Our code and data will be made available at https://github.com/ssuralcmu/BEVMapMatch.git.
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