通过合成鸟瞰图实现地面与空中图像的精准匹配，解决无GPS环境下的定位难题。
BEVLoc: Cross-View Localization and Matching via Birds-Eye-View Synthesis
- 利用对比学习与领域特异性难例挖掘，生成可匹配的鸟瞰图表示。
- 在森林等复杂环境下实现粗粒度到细粒度的逐级定位，定位准确率显著提升。
- 适用于无人导航、自动驾驶等户外场景，为无卫星定位提供新思路。
地面对空图像匹配是户外机器人中一项关键且具挑战性的任务，尤其在缺乏或不可靠的全球定位系统（GNSS）时更为重要。建筑物或密集森林等结构会造成干扰，需替代方案实现全局定位。核心难点在于克服地面与空中视角差异以实现可接受的定位精度。受自动驾驶领域的启发，本文提出一种新框架，通过合成鸟瞰图（BEV）场景表示，实现与航拍地图在非铺装环境中的匹配与定位。采用基于领域特定难例挖掘的对比学习训练网络，使合成的BEV与航拍图具有相似表征。推理阶段，采用粗到精的匹配策略，引导在航拍图中识别最可能的位置。实验结果表明，在语义多样性低的极端森林环境中取得初步良好效果。分析了模型在粗匹配与细匹配中的表现，评估其原始匹配能力及作为GNSS替代方案的有效性。本工作深入探索了非铺装地图定位问题，并为未来研究建立了基础基准。代码已开源：https://github.com/rpl-cmu/bevloc。
原文摘要 · Abstract (English)
Ground to aerial matching is a crucial and challenging task in outdoor robotics, particularly when GPS is absent or unreliable. Structures like buildings or large dense forests create interference, requiring GNSS replacements for global positioning estimates. The true difficulty lies in reconciling the perspective difference between the ground and air images for acceptable localization. Taking inspiration from the autonomous driving community, we propose a novel framework for synthesizing a birds-eye-view (BEV) scene representation to match and localize against an aerial map in off-road environments. We leverage contrastive learning with domain specific hard negative mining to train a network to learn similar representations between the synthesized BEV and the aerial map. During inference, BEVLoc guides the identification of the most probable locations within the aerial map through a coarse-to-fine matching strategy. Our results demonstrate promising initial outcomes in extremely difficult forest environments with limited semantic diversity. We analyze our model's performance for coarse and fine matching, assessing both the raw matching capability of our model and its performance as a GNSS replacement. Our work delves into off-road map localization while establishing a foundational baseline for future developments in localization. Our code is available at: https://github.com/rpl-cmu/bevloc
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。