地空跨视角定位研究综述

胡迪; 袁夏; 徐孝强; 赵春霞

doi:10.11999/JEIT250167

地空跨视角定位研究综述

doi: 10.11999/JEIT250167 cstr: 32379.14.JEIT250167

南京理工大学南京 210094

详细信息

作者简介:
胡迪：男，博士生，研究方向为跨视角定位、智能机器人

袁夏：男，副教授，研究方向为计算机视觉、机器人导航

徐孝强：男，硕士生，研究方向为视觉环境感知、智能机器人

赵春霞：女，教授，研究方向为模式识别、图像处理、人工智能

通讯作者:
袁夏　yuanxia@njust.edu.cn

中图分类号: TP751.1
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- 被引次数: 0
出版历程
- 录用日期: 2025-12-17
- 网络出版日期: 2025-12-25

A Review of Ground-to-Aerial Cross-View Localization Research

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

摘要

摘要: 地空跨视角定位指的是利用空中视角图像作为参考，确定待查询的地面传感器位姿的过程。随着无人驾驶汽车、无人机导航等技术的发展，地空跨视角定位技术在智能交通、城市管理等领域扮演着越来越重要的角色。地空跨视角定位技术的发展经历了几个阶段：从早期基于人工设计特征的传统方法，到深度学习技术的引入衍生出的度量学习、图像变换及图像生成方法，以及结合距离传感器的定位技术。这些技术的发展标志着从依赖手工特征到自动学习特征表示的转变，以及从单一模态到多模态数据融合的进步。尽管取得了丰富的成果，地空跨视角定位领域仍存在很多挑战，尤其是在处理时空差异导致的定位误差问题上。这些挑战包括季节变化、日夜交替、天气状况差异等时间维度上的差异，以及视角改变、场景布局变化等空间维度上的差异，这些都对算法的鲁棒性和准确性提出了更高的要求。本文聚焦于地空跨视角定位，系统梳理了主要方法、关键数据集和评价方法，并对未来的发展趋势进行了分析。同时，本文首次系统化地整理了结合距离传感器的地空跨视角定位算法，为该领域的研究提供了新的视角和思路。
- 地空跨视角定位 /
- 计算机视觉 /
- 深度学习 /
- 距离传感器 /
- 多模态
Abstract: Significance This paper provides a comprehensive review of ground to aerial cross view localization, systematically organizing the main methods, key datasets, and evaluation metrics. Notably, it is the first to systematically organize ground to aerial cross view localization algorithms combined with range sensors such as LiDAR and millimeter-wave radar, offering new perspectives and insights for future research. Ground to aerial cross view localization has emerged as a pivotal research area in computer vision, bridging the gap between ground-level and aerial imagery to determine the precise pose of ground-based sensors using aerial images as references. This technology is increasingly vital in applications such as autonomous driving, drone navigation, intelligent transportation, and urban management. Despite significant advancements, cross view localization faces substantial challenges due to temporal and spatial variations, such as seasonal changes, day-night transitions, weather conditions, viewpoint alterations, and scene layout modifications. These factors necessitate the development of more robust and accurate algorithms to mitigate localization errors. This paper not only summarizes the state-of-the-art in cross view localization but also provides a forward-looking perspective on the challenges and opportunities in the field. Progress The field of ground to aerial cross view localization has witnessed significant advancements, particularly with the integration of range sensors such as LiDAR and millimeter-wave radar, which has opened new avenues for research and application. The evolution of this field can be categorized into several stages. Initially, traditional methods relied on manually designed features for cross view localization, marking a paradigm shift from same-view to cross view geographic localization strategies. The advent of deep learning introduced a new era, with researchers leveraging metric learning, image transformation, and image generation techniques to learn correspondences between different viewpoint images. Despite these advancements, existing deep learning models often struggled with temporal and spatial variations, particularly in long-term cross-season scenarios where image appearances at the same location varied significantly across seasons. Additionally, the large scale nature of urban environments posed challenges in efficiently searching and matching images. Range sensors, such as LiDAR, provide precise distance measurements and 3D structural information, which are crucial for accurate localization, especially in environments where visual cues are unreliable or unavailable. However, the fusion of data from range sensors with visual data poses significant challenges, including differences in data resolution, frequency, and coverage. Conclusions This paper provides a comprehensive review of the development of ground to aerial cross view localization technologies, analyzing key technological breakthroughs and their underlying driving forces at various stages. It systematically categorizes and discusses the main methods of cross view localization from an algorithmic perspective, aiming to offer readers a clear theoretical framework and technical overview. Notably, the paper emphasizes the role of key datasets in propelling the field forward, comparing the performance of various cross view localization models on these datasets to highlight differences and advantages among methods, thereby providing valuable references for subsequent research. Despite significant advancements, several challenges remain in ground to aerial cross view localization. These include issues related to cross-region localization accuracy, localization precision over large scale aerial images, and the impact of temporal changes on geographic features. These challenges necessitate further research and innovation to enhance the robustness, accuracy, and efficiency of localization systems. Prospects Looking ahead, the future of ground to aerial cross view localization research is poised to focus on several key areas. Future research should delve deeper into effectively converting range sensor data into feature representations that align with image data, facilitating efficient cross-modal information fusion. This could involve the use of multi-branch network architectures where each branch processes a different data modality, followed by a fusion layer that combines these features to extract richer information. Additionally, graph models could be employed to train on shared semantics between ground and aerial views, thereby representing relationships across different data modalities and enabling effective cross-modal information propagation and fusion. There is also a pressing need to develop algorithms that can adapt to variations such as seasonal changes, day-night cycles, and different weather conditions, thereby improving the robustness and accuracy of localization systems. Moreover, integrating multi-scale and multi-temporal data could enhance the model’s adaptability to spatio-temporal variations, for instance, by combining high-resolution and low-resolution images or images captured at different times. For large scale urban environments, research should focus on developing efficient search and matching techniques to enhance localization efficiency. Parallel computing frameworks could be employed to handle large datasets, thereby improving the efficiency of search and matching processes. Algorithmically, strategies such as pruning could be implemented at various stages to reduce unnecessary computations, lower algorithm complexity, and improve matching efficiency on large datasets. In conclusion, while cross view localization faces several challenges, it also holds immense potential for development and application. Future research should continue to explore and innovate in areas such as multi-modal data fusion and efficient search and matching in large scale urban environments to further advance this field.
- Ground-to-aerial cross-view localization /
- Computer vision /
- Deep learning /
- Range sensors /
- Multimodal

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图 1 地空跨视角定位方法分类

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图 2 地空跨视角定位任务图示

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图 3 地空跨视角定位方法时间轴概述

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图 4 SAFA^[35]网络框架。算法首先将卫星图进行极坐标变换，将输出的转换卫星图与地面周视图进行特征匹配。

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图 5 S2SP^[40]网络框架，根据高度分布完成卫星图到街景图的几何投影，并通过生成器构建地面周视图，与真实地面周视图进行匹配。

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图 6 CVML^[41]网络框架。算法通过地面与卫星图像描述子间的相似度作为地空图像融合的一个特征，实现细粒度度量定位。

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图 7 面向道路结构的跨视角定位方法架构^[18]，分别提取地面与卫星视角下的路缘结构，并将其作为地空共享语义进行匹配并生成位姿。

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表 1 位置检索四种类别方法对比

维度	人工特征方法	度量学习方法	图像变换方法	图像生成方法
核心目的	基于设计特征进行关联匹配	学习判别性特征进行匹配	显式建模补偿视角几何差异	生成目标视图图像
主要驱动力	特征工程、先验模型	深度表征学习、优化目标	几何投影模型	生成对抗网络
核心价值	奠基性、可解释性	高精度、强大特征表示能力	增强几何鲁棒性	有效弥合视角域差异
核心创新	特定特征设计与利用	自动特征学习机制	几何变换规则	视角映射与图像合成
主要优势	计算要求低、设计目标精准	精度高、泛化性强	几何鲁棒性强	缩小域差距、无监督潜力大
主要局限	鲁棒性差、表达能力瓶颈	算力依赖高、几何建模隐式	简化现实几何变换畸变风险	生成质量依赖高、模型复杂
演进趋势	逐渐被深度学习模型替代	结合Transformer持续发展	解决特定几何挑战	探索域差异新解法

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表 2 跨视角定位常用数据集

名称	介绍	特点	视觉数据 (空-地)	有无 GNSS	有无 IMU	任务目标
CVUSA^[22]	涵盖纽约、洛杉矶、迈阿密等美国20个城市，包含35532对用于训练的图像和8884对用于测试的图像。	支持多尺度的跨视角训练，通过引入同一地理位置的多尺度航拍图，提高了模型在大规模应用场景中的定位精度。	图像-图像	无	无	检索定位
CVACT^[50]	覆盖了澳大利亚首都堪培拉的300平方英里的地理区域，具体包括了35532组训练用图像对和92802组测试用图像对。通过谷歌地图API采集地空图像对，并对每个街景图配备GNSS标签。	用于验证方法在更大地理范围内的跨视角定位实例上的泛化能力，并引入了一种全新的Siamese CNN架构，能够同时从外观和方向几何信息中学习特征嵌入。	图像-图像	有	无	检索定位
VIGOR^[21]	覆盖了美国4个主要城市(旧金山、纽约、西雅图和芝加哥)，收集了共计90618张航拍图像和238696张街景全景图。通过密集采样来实现区域的无缝覆盖，确保了即便街景图像出现在任意位置，至少被一张航拍图像覆盖。	突破现有数据集一对一检索限制，引入了航拍图像是在查询图像出现之前捕获这一更贴近实际场景的假设，构建了一个更加现实的跨视角地理定位问题框架，并首次从米级实际距离的角度评估了定位准确性。	图像-图像	有	无	检索定位/ 位姿回归
KITTI^[20]	在德国卡尔斯鲁厄周边不同交通场景下，通过地面车载传感器记录了长达6小时的交通数据，涵盖了公路、乡村到城市的多样场景，包含大量静态和动态物体。	场景趋于城乡多元化，融合了点云数据作为环境感知输入	图像-图像/ 图像-点云	有	有	位姿回归
Oxford RobotCar^[51,52]	通过一个固定的路线定期穿越中心牛津市区，平均每周两次，在19个月期间共记录了约1010公里的驾驶路程。数据集包括2000万张图像，这些图像来源于车辆上安装的六个摄像头，以及包括LiDAR、GNSS和惯性导航系统在内的地面实况数据	偏向于同区域内的长时跨季数据，采集期间的道路和建筑施工导致路线的某些部分从数据收集开始到结束发生了显著变化	图像-图像/ 图像-点云	有	有	位姿回归
Oxford Radar RobotCar^[53]	在Oxford RobotCar原平台基础上进行了扩展，在加装毫米波雷达的前提下，历时一个月在牛津中心遍历32圈，共计约280公里。通过毫米波雷达发射出的电磁波对雨、雾、夜间等场景重新扫描，实现相同区域内生成光学数据与电磁数据，彼此互补。	相较于Oxford RobotCar数据集，Oxford Radar RobotCar聚焦毫米波雷达，更偏向于鲁棒性与扫描范围，通过将雷达里程计与视觉-惯导-激光一起做全局优化后，得到的轨迹弥补了在城区漂移较大的缺陷。	图像-图像/ 图像-点云	有	有	位姿回归
Ford Multi- AV^[54]	由福特自动驾驶汽车车队V1、V2、V3三辆车辆在2017到2018年的不同日期和时间收集。这些车辆在密歇根州的平均行驶里程为66公里，涵盖多种驾驶场景。	包含了动态城市环境中天气、光照、建筑和交通条件的季节性变化，有助于设计的算法对季节和城市变化更具鲁棒性	图像-图像/ 图像-点云	有	有	位姿回归

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表 3 CVUSA和CVACT数据集模型表现

方法		CVUSA				CVACT
方法		r@1	r@5	r@10	r@1%	r@1	r@5	r@10	r@1%
度量学习	Workman等人^[22]	-	-	-	34.30	-	-	-	-
	Vo等人^[14]	-	-	-	63.70	-	-	-	-
	Zhai等人^[15]	-	-	-	43.20	-	-	-	-
	CVM-Net^[32]	22.47	49.98	63.18	93.62	5.41	14.79	25.63	54.53
	L2LTR^[55]	94.05	98.27	98.99	99.67	60.72	85.85	89.88	96.12
	TransGeo^[33]	94.08	98.36	99.04	99.77	-	-	-	-
	OR-CVFI^[58]	98.05	99.48	99.64	-	91.03	96.31	97.02	-
图像变换	GeoDTR+^[59]	95.40	98.44	99.05	99.75	67.57	89.84	92.57	98.54
	Liu等人^[50]	40.79	66.82	76.36	96.12	19.90	34.82	41.23	63.79
	AMPLE^[60]	93.22	97.90	98.76	99.75	85.69	93.42	94.66	97.58
	ArcGeo^[61]	97.47	99.48	-	99.67	90.90	95.84	96.77	-
	HADGEO^[62]	95.01	98.45	99.10	-	86.48	94.21	95.50	-
	SAFA^[35]	89.84	96.93	98.14	99.64	55.50	79.94	85.08	94.49
	Shi等人^[56]	91.96	97.50	98.54	99.67	35.55	60.17	67.95	86.71
图像生成	Regmi等人^[57]	48.75	-	81.27	95.98	-	-	-	-
图像生成	Toker等人^[26]	92.56	97.55	98.33	99.57	61.29	85.13	89.14	98.32

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表 4 VIGOR数据集模型表现

方法	同区域					跨区域
方法	r@1	r@5	r@10	r@1%	Hit rate	r@1	r@5	r@10	r@1%	Hit rate
SAFA^[35]	18.69	43.64	55.36	97.55	21.90	2.77	8.61	12.94	62.64	3.16
SAFA+Mining^[21]	38.02	62.87	71.12	97.63	41.81	9.23	21.12	28.02	77.84	9.92
VIGOR^[21]	41.07	65.81	74.05	98.37	44.71	11.00	23.56	30.76	80.22	11.64
TransGeo^[33]	61.48	87.54	91.88	99.56	73.09	18.99	38.24	46.91	88.94	21.21
SAIG^[64]	55.60	81.83	-	99.43	63.57	22.35	42.43	-	90.83	24.69
EP-BEV^[63]	82.18	97.10	98.17	99.70	-	72.19	88.68	91.68	98.56	-
InfoNCE^[65]	76.15	94.46	96.28	99.53	87.24	-	-	-	-	-
GeoDTR^[66]	56.51	80.37	86.21	99.25	61.76	30.02	52.67	61.45	94.40	30.19
GeoDTR+^[59]	59.01	81.77	87.10	99.07	67.41	36.01	59.06	67.22	94.95	39.40

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表 5 KITTI数据集模型表现

类型	方法	有无先验	同区域				跨区域
			均值误差		中值误差		均值误差		中值误差
			距离(m)	角度(°)	距离(m)	角度(°)	距离(m)	角度(°)	距离(m)	角度(°)
图像到图像	LM^[67]	√	12.08	3.72	11.42	2.83	12.58	3.95	12.11	3.03
	SliceMatch^[68]	√	7.96	4.12	4.39	3.65	13.50	4.20	9.77	6.61
	CCVPE^[69]	√	1.22	0.67	0.62	0.54	9.16	1.55	3.33	0.84
	HC-Net^[70]	√	0.80	0.45	0.50	0.33	8.47	3.22	4.57	1.63
	BevSplat^[71]	√	2.86	0.33	2.00	0.28	6.24	0.33	2.68	0.28
	Petalview^[72]	√	2.10	3.94	-	-	-	-	-	-
	CVR^[21]	√	9.36	-	8.49	-	-	-	-	-
	CVML^[41]	√	1.40	-	0.99	-	-	-	-	-
	Song等人^[42]	√	1.48	0.49	0.47	0.30	7.97	2.17	3.52	1.21
	LM^[67]	×	15.51	89.91	15.97	90.75	15.50	89.84	16.02	89.85
	SliceMatch^[68]	×	9.39	8.71	5.41	4.42	14.85	23.64	11.85	7.96
	CCVPE^[69]	×	6.88	15.01	3.47	6.12	13.94	77.84	10.98	63.84
	Petalview^[72]	×	2.32	8.86	-	-	23.79	57.10	-	-
图像到雷达	RSL-Net^[44]	×	-	-	-	-	3.71	1.59	-	-
	Tang等人^[73]	×	-	-	-	-	4.37	1.67	-	-
	Tang等人^[47]	×	-	-	-	-	4.02	3.70	-	-
	Tang等人^[48]	×	-	-	-	-	3.76	3.36	-	-
	AVBM^[74]	×	-	-	-	-	4.96	5.13	4.04	4.22
	Hu等人^[18]	×	-	-	-	-	3.66	1.85	2.95	1.62

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