Accurate geo-localization of Unmanned Aerial Vehicles (UAVs) is crucial for a
variety of outdoor applications including search and rescue operations, power
line inspections, and environmental monitoring. The vulnerability of Global
Navigation Satellite Systems (GNSS) signals to interference and spoofing
necessitates the development of additional robust localization methods for
autonomous navigation. Visual Geo-localization (VG), leveraging onboard cameras
and reference satellite maps, offers a promising solution for absolute
localization. Specifically, Thermal Geo-localization (TG), which relies on
image-based matching between thermal imagery with satellite databases, stands
out by utilizing infrared cameras for effective night-time localization.
However, the efficiency and effectiveness of current TG approaches, are
hindered by dense sampling on satellite maps and geometric noises in thermal
query images. To overcome these challenges, in this paper, we introduce STHN, a
novel UAV thermal geo-localization approach that employs a coarse-to-fine deep
homography estimation method. This method attains reliable thermal
geo-localization within a 512-meter radius of the UAV's last known location
even with a challenging 11% overlap between satellite and thermal images,
despite the presence of indistinct textures in thermal imagery and self-similar
patterns in both spectra. Our research significantly enhances UAV thermal
geo-localization performance and robustness against the impacts of geometric
noises under low-visibility conditions in the wild. The code will be made
publicly available.

STHN 是一种新的无人机热定位方法，通过粗糙到精细的深度单应性估计方法，在低能见度条件下可可在无人机上次已知位置的 512 米范围内实现可靠的热定位，克服了热影像模糊纹理和自相似模式的困扰，显著提高了无人机热定位性能和鲁棒性。

STHN: 无人机热红外图像与卫星影像的深度齐次估计与地理定位

STHN: Deep Homography Estimation for UAV Thermal Geo-localization with  Satellite Imagery

Thanks to the complementary nature of millimeter wave radar and camera, deep
learning-based radar-camera 3D object detection methods may reliably produce
accurate detections even in low-visibility conditions. This makes them
preferable to use in autonomous vehicles' perception systems, especially as the
combined cost of both sensors is cheaper than the cost of a lidar. Recent
radar-camera methods commonly perform feature-level fusion which often involves
projecting the radar points onto the same plane as the image features and
fusing the extracted features from both modalities. While performing fusion on
the image plane is generally simpler and faster, projecting radar points onto
the image plane flattens the depth dimension of the point cloud which might
lead to information loss and makes extracting the spatial features of the point
cloud harder. We proposed ClusterFusion, an architecture that leverages the
local spatial features of the radar point cloud by clustering the point cloud
and performing feature extraction directly on the point cloud clusters before
projecting the features onto the image plane. ClusterFusion achieved the
state-of-the-art performance among all radar-monocular camera methods on the
test slice of the nuScenes dataset with 48.7% nuScenes detection score (NDS).
We also investigated the performance of different radar feature extraction
strategies on point cloud clusters: a handcrafted strategy, a learning-based
strategy, and a combination of both, and found that the handcrafted strategy
yielded the best performance. The main goal of this work is to explore the use
of radar's local spatial and point-wise features by extracting them directly
from radar point cloud clusters for a radar-monocular camera 3D object
detection method that performs cross-modal feature fusion on the image plane.

利用微波雷达和摄像机的互补性，基于深度学习的雷达摄像机三维物体检测方法可以可靠地在低能见度条件下生成准确的检测结果。这使得它们成为自动驾驶车辆感知系统中的首选，特别是由于两种传感器的综合成本比激光雷达更便宜。我们提出了 ClusterFusion，一种利用雷达点云的局部空间特征的架构，在将特征投影到图像平面之前，对雷达点云聚类进行特征提取。ClusterFusion 在 nuScenes 数据集的测试集中取得了最先进的性能，nuScenes 检测分数（NDS）为 48.7％。我们还研究了不同雷达特征提取策略在点云聚类上的性能：一种手工策略，一种基于学习的策略以及两者的组合，并发现手工策略提供了最佳的性能。本研究的主要目标是探索使用雷达点云聚类直接提取雷达的局部空间和逐点特征，用于在图像平面上进行交叉模态特征融合的雷达 - 单目摄像机三维物体检测方法。

ClusterFusion: 在自动驾驶车辆中利用雷达空间特征进行雷达 - 相机三维物体检测

ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D  Object Detection in Autonomous Vehicles

Sensor fusion is a crucial augmentation technique for improving the accuracy
and reliability of perception systems for automated vehicles under diverse
driving conditions. However, adverse weather and low-light conditions remain
challenging, where sensor performance degrades significantly, exposing vehicle
safety to potential risks. Advanced sensors such as LiDARs can help mitigate
the issue but with extremely high marginal costs. In this paper, we propose a
novel transformer-based 3D object detection model "REDFormer" to tackle low
visibility conditions, exploiting the power of a more practical and
cost-effective solution by leveraging bird's-eye-view camera-radar fusion.
Using the nuScenes dataset with multi-radar point clouds, weather information,
and time-of-day data, our model outperforms state-of-the-art (SOTA) models on
classification and detection accuracy. Finally, we provide extensive ablation
studies of each model component on their contributions to address the
above-mentioned challenges. Particularly, it is shown in the experiments that
our model achieves a significant performance improvement over the baseline
model in low-visibility scenarios, specifically exhibiting a 31.31% increase in
rainy scenes and a 46.99% enhancement in nighttime scenes.The source code of
this study is publicly available.

本篇论文提出了一种基于变形金刚网络的 3D 物体检测模型 REDFormer，该模型通过使用鸟瞰相机和雷达融合，解决低能见度条件下自动驾驶系统的感知问题，并且在多雷达点云数据、多种天气和时间数据的 nuScenes 数据集上进行验证，相比其他最先进模型，该模型在低能见度情况下表现出更高的检测精度。