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 work addresses visual cross-view metric localization for outdoor
robotics. Given a ground-level color image and a satellite patch that contains
the local surroundings, the task is to identify the location of the ground
camera within the satellite patch. Related work addressed this task for
range-sensors (LiDAR, Radar), but for vision, only as a secondary regression
step after an initial cross-view image retrieval step. Since the local
satellite patch could also be retrieved through any rough localization prior
(e.g. from GPS/GNSS, temporal filtering), we drop the image retrieval objective
and focus on the metric localization only. We devise a novel network
architecture with denser satellite descriptors, similarity matching at the
bottleneck (rather than at the output as in image retrieval), and a dense
spatial distribution as output to capture multi-modal localization ambiguities.
We compare against a state-of-the-art regression baseline that uses global
image descriptors. Quantitative and qualitative experimental results on the
recently proposed VIGOR and the Oxford RobotCar datasets validate our design.
The produced probabilities are correlated with localization accuracy, and can
even be used to roughly estimate the ground camera's heading when its
orientation is unknown. Overall, our method reduces the median metric
localization error by 51%, 37%, and 28% compared to the state-of-the-art when
generalizing respectively in the same area, across areas, and across time.

本文使用新型网络结构和更密集的卫星图像描述符进行多模式区域定位，可将度量定位误差中位数相对于同一区域、跨区域和跨时间的现有技术分别降低 51％、37％和 28％。

使用稠密不确定度估计的视觉跨视图度量定位

Visual Cross-View Metric Localization with Dense Uncertainty Estimates

Appearance changes due to weather and seasonal conditions represent a strong
impediment to the robust implementation of machine learning systems in outdoor
robotics. While supervised learning optimises a model for the training domain,
it will deliver degraded performance in application domains that underlie
distributional shifts caused by these changes. Traditionally, this problem has
been addressed via the collection of labelled data in multiple domains or by
imposing priors on the type of shift between both domains. We frame the problem
in the context of unsupervised domain adaptation and develop a framework for
applying adversarial techniques to adapt popular, state-of-the-art network
architectures with the additional objective to align features across domains.
Moreover, as adversarial training is notoriously unstable, we first perform an
extensive ablation study, adapting many techniques known to stabilise
generative adversarial networks, and evaluate on a surrogate classification
task with the same appearance change. The distilled insights are applied to the
problem of free-space segmentation for motion planning in autonomous driving.

天气和季节变化对机器学习在户外机器人系统中的稳定实现产生了强烈的阻碍，为了解决这个问题，本文提出了一种以对抗技术为基础的无监督领域自适应框架，并应用于自动驾驶中解决自由空间分割问题。