We present AGL-NET, a novel learning-based method for global localization
using LiDAR point clouds and satellite maps. AGL-NET tackles two critical
challenges: bridging the representation gap between image and points modalities
for robust feature matching, and handling inherent scale discrepancies between
global view and local view. To address these challenges, AGL-NET leverages a
unified network architecture with a novel two-stage matching design. The first
stage extracts informative neural features directly from raw sensor data and
performs initial feature matching. The second stage refines this matching
process by extracting informative skeleton features and incorporating a novel
scale alignment step to rectify scale variations between LiDAR and map data.
Furthermore, a novel scale and skeleton loss function guides the network toward
learning scale-invariant feature representations, eliminating the need for
pre-processing satellite maps. This significantly improves real-world
applicability in scenarios with unknown map scales. To facilitate rigorous
performance evaluation, we introduce a meticulously designed dataset within the
CARLA simulator specifically tailored for metric localization training and
assessment. The code and dataset will be made publicly available.

通过利用 LiDAR 点云和卫星地图，AGL-NET 提出了一种新的基于学习的全球定位方法，解决了图像和点云数据之间的表征差异以及全局视图和局部视图之间的尺度差异的关键挑战。AGL-NET 采用一种统一的网络架构，通过创新的两阶段匹配设计来提取信息化的神经特征，并引入了新颖的尺度对齐步骤来校正 LiDAR 和地图数据之间的尺度变化，进一步通过新颖的尺度和骨骼损失函数指导网络学习尺度不变的特征表征，从而消除了预处理卫星地图的需要，显著提高了在未知地图尺度情况下的实际应用性。为了促进严格的性能评估，我们在 CARLA 模拟器中引入了一个精心设计的数据集，专门用于度量定位训练和评估。代码和数据集将公开提供。

AGL-NET：具有不同尺度的航地跨模态全局定位

AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying  Scales

High-Definition (HD) maps play a crucial role in autonomous driving systems.
Recent methods have attempted to construct HD maps in real-time based on
information obtained from vehicle onboard sensors. However, the performance of
these methods is significantly susceptible to the environment surrounding the
vehicle due to the inherent limitation of onboard sensors, such as weak
capacity for long-range detection. In this study, we demonstrate that
supplementing onboard sensors with satellite maps can enhance the performance
of HD map construction methods, leveraging the broad coverage capability of
satellite maps. For the purpose of further research, we release the satellite
map tiles as a complementary dataset of nuScenes dataset. Meanwhile, we propose
a hierarchical fusion module that enables better fusion of satellite maps
information with existing methods. Specifically, we design an attention mask
based on segmentation and distance, applying the cross-attention mechanism to
fuse onboard Bird's Eye View (BEV) features and satellite features in
feature-level fusion. An alignment module is introduced before concatenation in
BEV-level fusion to mitigate the impact of misalignment between the two
features. The experimental results on the augmented nuScenes dataset showcase
the seamless integration of our module into three existing HD map construction
methods. It notably enhances their performance in both HD map semantic
segmentation and instance detection tasks.

在自动驾驶系统中，通过利用卫星地图作为车载传感器的补充，可以提高高清地图构建方法的性能，并且通过引入分层融合模块，可以有效地融合车载鸟瞰图和卫星图像，在高清地图语义分割和实例检测任务中显著提升了性能。