1.武汉大学测绘遥感信息工程全国重点实验室,湖北 武汉,430079
2.中国卫星导航定位应用管理中心,北京,100088
3.信息工程大学地理空间信息学院,河南 郑州,450001
4.陆军研究院,江苏 无锡,214035
张过,博士,教授,研究方向为航天摄影测量。guozhang@whu.edu.cn
收稿:2026-07-02,
纸质出版:2026-10-05
移动端阅览
张过,王善绣,徐青,等.陆域无路导航理论方法与关键技术[J].武汉大学学报(信息科学版),2026,51(10):2167-2179.
ZHANG Guo,WANG Shanxiu,XU Qing,et al.Theory, Methods and Key Technologies of Terrestrial Off-Road Navigation[J].Geomatics and Information Science of Wuhan University,2026,51(10):2167-2179.
张过,王善绣,徐青,等.陆域无路导航理论方法与关键技术[J].武汉大学学报(信息科学版),2026,51(10):2167-2179. DOI:10.13203/j.whugis20260301
ZHANG Guo,WANG Shanxiu,XU Qing,et al.Theory, Methods and Key Technologies of Terrestrial Off-Road Navigation[J].Geomatics and Information Science of Wuhan University,2026,51(10):2167-2179. DOI:10.13203/j.whugis20260301
经典车辆导航系统以道路网络为基础,通过构建道路网拓扑关系以及实时道路交通情况按照交通规则实现路径规划与引导服务,发展已相对成熟。然而在复杂自然环境、灾害应急救援以及野外生产作业等场景中,道路缺失、失能或道路与非道路区域交替出现,经典道路导航难以满足机动需求。聚焦航天遥感支撑的陆域无路导航理论方法,明确无路导航的基本概念、应用需求,围绕陆域通行要素提取的可靠性、时变条件下车辆通过性认知和任务驱动的规划决策引导3个科学问题,梳理“环境感知测量—通过性认知预测—规划决策引导”的理论框架,讨论通行要素遥感智能解译、多尺度通过性动态建模、多主体协同规划与引导及导航服务等关键技术。对地表覆盖数据、几何通过性及支承通过性模型的验证结果分析表明,基于土壤力学参数约束的支承通过性模型能够较好预测车辆速度,实现了从航天遥感信息到车辆通过能力评价的转换。现有结果为陆域通行要素获取和车辆通过性评价提供了依据,为航天遥感信息向陆域导航应用的转化提供分析框架与技术参考。
Objectives
2
Conventional vehicle navigation systems are well established
providing route planning and guidance based on road network topology
real-time traffic conditions
and traffic rules. However
they cannot adequately meet mobility requirements in complex natural environments
disaster response
and field operations where roads are absent
unusable
or interspersed with off-road areas.
Methods
2
This paper proposes the theory and methods of terrestrial off-road navigation supported by spaceborne remote sensing. The basic concepts and application requirements of off-road navigation are clarified. Three scientific questions are examined
including reliable extraction of terrestrial traversability-related factors
vehicle traversability assessment under time-varying conditions
and the task-driven planning
decision-making and guidance. A theoretical framework comprising environmental perception and measurement
traversability assessment and prediction
and planning
decision-making
and guidance is outlined. Key technologies are discussed
including intelligent interpretation of traversability-related factors from remote sensing data
multiscale dynamic traversability modeling
multi-agent collaborative planning and guidance
and navigation services. A tracked-vehicle field test in the Chuzhou test area is used to analyze validation results for land-cover data
geometric traversability
and traversability models based on soil bearing capacity.
Results
2
The traversability model constrained by soil mechanical parameters predicts vehicle speed reasonably well
demonstrating the translation of spaceborne remote sensing information into vehicle traversability assessment. The available results provide evidence supporting terrestrial traversability-related factor acquisition and vehicle traversability evaluation.
Conclusions
2
This study provides an analytical framework and technical reference for applying spaceborne remote sensing information to terrestrial navigation
with traversability assessment connecting environmental observations to vehicle mobility evaluation.
NAHAVANDI S , ALIZADEHSANI R , NAHAVANDI D , et al . A Comprehensive Review on Autonomous Navigation [J]. ACM Computing Surveys , 2025 , 57 ( 9 ): 1 - 67 .
PADEN B , ČÁP M , YONG S Z , et al . A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles [J]. IEEE Transactions on Intelligent Vehicles , 2016 , 1 ( 1 ): 33 - 55 .
KENDOUL F . Survey of Advances in Guidance, Navigation, and Control of Unmanned Rotorcraft Systems [J]. Journal of Field Robotics , 2012 , 29 ( 2 ): 315 - 378 .
KAPLAN E D , HEGARTY C J . Understanding GPS/GNSS: Principles and Applications [M]. 3rd ed . Boston : Artech House , 2017 .
GROVES P D . Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems [M]. 2nd ed . Boston : Artech House , 2013 .
STUMPF R P , HOLDERIED K , SINCLAIR M . Determination of Water Depth with High-Resolution Satellite Imagery over Variable Bottom Types [J]. Limnology and Oceanography , 2003 , 48 (1part 2 ): 547 - 556 .
SANDWELL D T , SMITH W H F . Marine Gravity Anomaly from Geosat and ERS 1 Satellite Altimetry [J]. Journal of Geophysical Research: Solid Earth , 1997 , 102 ( B5 ): 10039 - 10054 .
RICHARDS J A , JIA X P . Remote Sensing Digital Image Analysis: An Introduction [M]. Heidelberg : Springer , 2006 .
MAXWELL A E , WARNER T A , FANG F . Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review [J]. International Journal of Remote Sensing , 2018 , 39 ( 9 ): 2784 - 2817 .
LEHTOLA V V , KOEVA M , ELBERINK S O , et al . Digital Twin of a City: Review of Technology Serving City Needs [J]. International Journal of Applied Earth Observation and Geoinformation , 2022 , 114 : 102915 .
REICHSTEIN M , CAMPS-VALLS G , STEVENS B , et al . Deep Learning and Process Understanding for Data-Driven Earth System Science [J]. Nature , 2019 , 566 ( 7743 ): 195 - 204 .
ZHOU N , ZHANG G , ZHU C Y , et al . An Unstructured Roadless Environment Navigation Map Construction Method Based on Remote Sensing [J]. Geo⁃spatial Information Science , 2026 , 29 ( 3 ): 1897 - 1918 .
EDER M , PRINZ R , SCHÖGGL F , et al . Traversability Analysis for Off-Road Environments Using Locomotion Experiments and Earth Observation Data [J]. Robotics and Autonomous Systems , 2023 , 168 : 104494 .
MUSAU H , RUGANUZA D , INDAH D , et al . A Review of Off-Road Datasets, Sensor Technologies and Terrain Traversability Analysis [J]. SAE International Journal of Advances and Current Practices in Mobility , 2025 , 7 ( 5 ): 2568 - 2588 .
E CARVALHO A , PORTUGAL D , PEIXOTO P . On Terrain Traversability Analysis in Unstructured Environments: Recent Advances in Forest Applications [J]. Intelligent Service Robotics , 2025 , 18 ( 2 ): 195 - 213 .
ZHU Y J , WAN HASAN W Z , HARUN RAMLI H R , et al . Deep Reinforcement Learning of Mobile Robot Navigation in Dynamic Environment: A Review [J]. Sensors , 2025 , 25 ( 11 ): 3394 .
ARAFIN T , HOSEN A , NAJDOVSKI Z , et al . Advances and Trends in Terrain Classification Methods for Off-Road Perception [J]. Journal of Field Robotics , 2025 , 42 ( 7 ): 3515 - 3544 .
PAPADAKIS P . Terrain Traversability Analysis Methods for Unmanned Ground Vehicles: A Survey [J]. Engineering Applications of Artificial Intelligence , 2013 , 26 ( 4 ): 1373 - 1385 .
刘经南 , 郭文飞 , 郭迟 , 等 . 智能时代泛在测绘的再思考 [J]. 测绘学报 , 2020 , 49 ( 4 ): 403 - 414 .
LIU Jingnan , GUO Wenfei , GUO Chi , et al . Rethinking Ubiquitous Mapping in the Intelligent Age [J]. Acta Geodaetica et Cartographica Sinica , 2020 , 49 ( 4 ): 403 - 414 .
裴宏宇 , 杨功流 , 全伟 , 等 . 基于数值微分计算的SERF原子自旋惯性测量动态仿真 [J]. 导航定位与授时 , 2021 , 8 ( 2 ): 1 - 7 .
PEI Hongyu , YANG Gongliu , QUAN Wei , et al . Dynamic Simulation of SERF Atomic Spin Inertial Measurement Based on Numerical Differential Algorithm [J]. Navigation Positioning and Timing , 2021 , 8 ( 2 ): 1 - 7 .
张过 , 朱春阳 , 周宁 , 等 . 航天遥感无路导航数字路网构建与初步验证 [J]. 上海航天(中英文) , 2026 , 43 ( 2 ): 1 - 15 .
ZHANG Guo , ZHU Chunyang , ZHOU Ning , et al . Construction and Preliminary Verification of Aerospace Remote Sensing-Based Digital Road Network for Roadless Navigation [J]. Aerospace Shanghai (Chinese & English) , 2026 , 43 ( 2 ): 1 - 15 .
WU K , ZHANG Y Y , RU L X , et al . A Semantic-Enhanced Multi-Modal Remote Sensing Foundation Model for Earth Observation [J]. Nature Machine Intelligence , 2025 , 7 ( 8 ): 1235 - 1249 .
张过 , 秦绪文 , 朱春阳 , 等 . 从有路导航走向无路导航 [J]. 自然资源遥感 , 2024 , 36 ( 4 ): 1 - 8 .
ZHANG Guo , QIN Xuwen , ZHU Chunyang , et al . Navigation: From On-Road to Off-Road [J]. Remote Sensing for Natural Resources , 2024 , 36 ( 4 ): 1 - 8 .
LONGLEY P A , GOODCHILD M F , MAGUIRE D J , et al . Geographic Information Science and Systems [M]. 4th ed . West Sussex, UK : Wiley & Sons , 2015 .
ZHU X X , TUIA D , MOU L C , et al . Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources [J]. IEEE Geoscience and Remote Sensing Magazine , 2017 , 5 ( 4 ): 8 - 36 .
XIAO A R , XUAN W H , WANG J J , et al . Foundation Models for Remote Sensing and Earth Observation: A Survey [J]. IEEE Geoscience and Remote Sensing Magazine , 2025 , 13 ( 4 ): 297 - 324 .
朱春阳 , 张过 , 周宁 , 等 . 无道路环境车辆支承通过性全遥感反演方法 [J]. 武汉大学学报(信息科学版) , 2025 , 50 ( 11 ): 2247 - 2259 .
ZHU Chunyang , ZHANG Guo , ZHOU Ning , et al . Remote Sensing-Based Inversion Method for Vehicle Trafficability in Off-Road Environments [J]. Geomatics and Information Science of Wuhan University , 2025 , 50 ( 11 ): 2247 - 2259 .
0
浏览量
2
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621