Accurate vehicle localization is a crucial step towards building effective
Vehicle-to-Vehicle networks and automotive applications. Yet standard grade GPS
data, such as that provided by mobile phones, is often noisy and exhibits
significant localization errors in many urban areas. Approaches for accurate
localization from imagery often rely on structure-based techniques, and thus
are limited in scale and are expensive to compute. In this paper, we present a
scalable visual localization approach geared for real-time performance. We
propose a hybrid coarse-to-fine approach that leverages visual and GPS location
cues. Our solution uses a self-supervised approach to learn a compact road
image representation. This representation enables efficient visual retrieval
and provides coarse localization cues, which are fused with vehicle ego-motion
to obtain high accuracy location estimates. As a benchmark to evaluate the
performance of our visual localization approach, we introduce a new large-scale
driving dataset based on video and GPS data obtained from a large-scale network
of connected dash-cams. Our experiments confirm that our approach is highly
effective in challenging urban environments, reducing localization error by an
order of magnitude.

本文提出了一种面向实时性能的可扩展视觉定位方法，该方法使用自我监督方法学习紧凑的路图像表示，结合车辆本体运动得出高精度位置估计，在挑战性的城市环境中有效地减少了定位误差一个数量级。

汽车应用的精准视觉定位

Accurate Visual Localization for Automotive Applications

In this paper, we propose the USIP detector: an Unsupervised Stable Interest
Point detector that can detect highly repeatable and accurately localized
keypoints from 3D point clouds under arbitrary transformations without the need
for any ground truth training data. Our USIP detector consists of a feature
proposal network that learns stable keypoints from input 3D point clouds and
their respective transformed pairs from randomly generated transformations. We
provide degeneracy analysis of our USIP detector and suggest solutions to
prevent it. We encourage high repeatability and accurate localization of the
keypoints with a probabilistic chamfer loss that minimizes the distances
between the detected keypoints from the training point cloud pairs. Extensive
experimental results of repeatability tests on several simulated and real-world
3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP
detector significantly outperforms existing hand-crafted and deep
learning-based 3D keypoint detectors. Our code is available at the project
website. this https URL

本文提出了一种 USIP 检测器，可在任意变换下从 3D 点云中检测高度重复和准确定位的关键点，而无需任何基础训练数据。通过使用多个验证数据集的实验，我们证明该方法在关键点检测上优于其他手工和深度学习的方法。