This paper presents a novel deep-learning-based approach to improve
localizing radar measurements against lidar maps. Although the state of the art
for localization is matching lidar data to lidar maps, radar has been
considered as a promising alternative, as it is potentially more resilient
against adverse weather such as precipitation and heavy fog. To make use of
existing high-quality lidar maps, while maintaining performance in adverse
weather, matching radar data to lidar maps is of interest. However, owing in
part to the unique artefacts present in radar measurements, radar-lidar
localization has struggled to achieve comparable performance to lidar-lidar
systems, preventing it from being viable for autonomous driving. This work
builds on an ICP-based radar-lidar localization system by including a learned
preprocessing step that weights radar points based on high-level scan
information. Combining a proven analytical approach with a learned weight
reduces localization errors in radar-lidar ICP results run on real-world
autonomous driving data by up to 54.94% in translation and 68.39% in rotation,
while maintaining interpretability and robustness.

本文提出一种基于深度学习的新型方法，改进了基于雷达测量的定位技术，并成功将雷达数据与激光雷达地图进行匹配，从而在降雨和大雾等恶劣天气条件下保持高性能，降低雷达 - 激光雷达定位中的误差。

指明方向：使用学习的 ICP 权重来改进雷达 - 激光雷达定位

Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP  Weights

Light-weight camera localization in existing maps is essential for
vision-based navigation. Currently, visual and visual-inertial odometry
(VO\&VIO) techniques are well-developed for state estimation but with
inevitable accumulated drifts and pose jumps upon loop closure. To overcome
these problems, we propose an efficient monocular camera localization method in
prior LiDAR maps using direct 2D-3D line correspondences. To handle the
appearance differences and modality gaps between LiDAR point clouds and images,
geometric 3D lines are extracted offline from LiDAR maps while robust 2D lines
are extracted online from video sequences. With the pose prediction from VIO,
we can efficiently obtain coarse 2D-3D line correspondences. Then the camera
poses and 2D-3D correspondences are iteratively optimized by minimizing the
projection error of correspondences and rejecting outliers. Experimental
results on the EurocMav dataset and our collected dataset demonstrate that the
proposed method can efficiently estimate camera poses without accumulated
drifts or pose jumps in structured environments.

本文提出了一种基于 2D-3D 线对应的高效单目相机在先前 LiDAR 地图中的定位方法，并使用位姿预测和离群值排除来优化相机位姿和 2D-3D 对应关系的投影误差，实验结果表明该方法可以在结构化环境中高效地估计相机位姿。