Feature-based geo-localization relies on associating features extracted from
aerial imagery with those detected by the vehicle's sensors. This requires that
the type of landmarks must be observable from both sources. This no-variety of
feature types generates poor representations that lead to outliers and
deviations, produced by ambiguities and lack of detections respectively. To
mitigate these drawbacks, in this paper, we present a dynamically weighted
factor graph model for the vehicle's trajectory estimation. The weight
adjustment in this implementation depends on information quantification in the
detections performed using a LiDAR sensor. Also, a prior (GNSS-based) error
estimation is included in the model. Then, when the representation becomes
ambiguous or sparse, the weights are dynamically adjusted to rely on the
corrected prior trajectory, mitigating in this way outliers and deviations. We
compare our method against state-of-the-art geo-localization ones in a
challenging ambiguous environment, where we also cause detection losses. We
demonstrate mitigation of the mentioned drawbacks where the other methods fail.

基于特征的地理定位依赖于将从航空影像中提取的特征与车辆传感器检测到的特征进行关联。本文中，我们提出了一种基于动态加权因子图模型的车辆轨迹估计方法，通过使用 LiDAR 传感器执行的检测中的信息量化来调整权重。同时，在模型中包含了基于 GNSS 的先验（错误）估计。当表示变得模糊或稀疏时，权重会动态调整以依赖于修正后的先验轨迹，从而减少异常值和偏差。我们在一个具有挑战性的模糊环境中与最先进的地理定位方法进行比较，并在其中引起检测损失。我们证明了在其他方法失败的情况下，我们的方法可以减轻所述缺点。