This paper proposes a fine-grained self-localization method for outdoor
robotics that utilizes a flexible number of onboard cameras and readily
accessible satellite images. The proposed method addresses limitations in
existing cross-view localization methods that struggle to handle noise sources
such as moving objects and seasonal variations. It is the first sparse
visual-only method that enhances perception in dynamic environments by
detecting view-consistent key points and their corresponding deep features from
ground and satellite views, while removing off-the-ground objects and
establishing homography transformation between the two views. Moreover, the
proposed method incorporates a spatial embedding approach that leverages camera
intrinsic and extrinsic information to reduce the ambiguity of purely visual
matching, leading to improved feature matching and overall pose estimation
accuracy. The method exhibits strong generalization and is robust to
environmental changes, requiring only geo-poses as ground truth. Extensive
experiments on the KITTI and Ford Multi-AV Seasonal datasets demonstrate that
our proposed method outperforms existing state-of-the-art methods, achieving
median spatial accuracy errors below $0.5$ meters along the lateral and
longitudinal directions, and a median orientation accuracy error below 2
degrees.

本文提出了一种细粒度的室外机器人自定位方法，利用可调数量的机载摄像头和易获取的卫星图像；该方法解决了现有的跨视角定位方法因移动物体和季节变化等噪声源的问题；通过从地面和卫星视图中检测一致的关键点和它们对应的深度特征，去除离地物体，并在两个视图之间建立同伦变换，该方式是第一个仅利用视觉的稀疏方法，以提高动态环境下的感知能力；此外，该方法采用空间嵌入方法，利用相机内外参数信息降低纯视觉匹配的歧义性，从而提高特征匹配和姿态估计的准确性。通过在 KITTI 和 Ford Multi-AV Seasonal 数据集上进行大量实验，证明了该方法优于现有的最先进方法，能够实现横向和纵向方向上低于 0.5 米的中位空间准确性误差，以及小于 2 度的中位方向准确性误差。

视图一致净化：实现准确的跨视图定位

View Consistent Purification for Accurate Cross-View Localization

This paper proposes a novel message passing neural (MPN) architecture
Conv-MPN, which reconstructs an outdoor building as a planar graph from a
single RGB image. Conv-MPN is specifically designed for cases where nodes of a
graph have explicit spatial embedding. In our problem, nodes correspond to
building edges in an image. Conv-MPN is different from MPN in that 1) the
feature associated with a node is represented as a feature volume instead of a
1D vector; and 2) convolutions encode messages instead of fully connected
layers. Conv-MPN learns to select a true subset of nodes (i.e., building edges)
to reconstruct a building planar graph. Our qualitative and quantitative
evaluations over 2,000 buildings show that Conv-MPN makes significant
improvements over the existing fully neural solutions. We believe that the
paper has a potential to open a new line of graph neural network research for
structured geometry reconstruction.

本文提出了一种新颖的消息传递神经网络（MPN）架构 Conv-MPN，从一张单一的 RGB 图像中重构室外建筑物为平面图，并与现有的全神经网络方案相比取得了显着的改进，该架构为图形神经网络研究开辟了新的领域。