面向无人车跨场景机动的多源融合定位技术

于宗骐; 冉启顺; 刘刚; 崔晓伟; 陆明泉

doi:10.12382/bgxb.2025.0527

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面向无人车跨场景机动的多源融合定位技术

更新时间：2026-04-24

- 面向无人车跨场景机动的多源融合定位技术
- Design and Validation of a Multisource Fusion Positioning Technology for Cross-scenario Maneuvering Autonomous Vehicles
- 兵工学报 2026年47卷第1期页码：250527
- 作者机构：
  
  清华大学电子工程系，北京 100084
- 作者简介：
  
  *通信作者邮箱：liu_gang@tsinghua.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2021YFA0716603)
- DOI：10.12382/bgxb.2025.0527
  中图分类号： TP242.6
- 收稿：2025-06-20，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
移动端阅览
于宗骐, 冉启顺, 刘刚, 等. 面向无人车跨场景机动的多源融合定位技术[J]. 兵工学报, 2026,47(1):250527.

YU Zongqi, RAN Qishun, LIU Gang, et al. Design and Validation of a Multisource Fusion Positioning Technology for Cross-scenario Maneuvering Autonomous Vehicles[J]. Acta Armamentarii, 2026, 47(1): 250527.
于宗骐, 冉启顺, 刘刚, 等. 面向无人车跨场景机动的多源融合定位技术[J]. 兵工学报, 2026,47(1):250527. DOI： 10.12382/bgxb.2025.0527.

YU Zongqi, RAN Qishun, LIU Gang, et al. Design and Validation of a Multisource Fusion Positioning Technology for Cross-scenario Maneuvering Autonomous Vehicles[J]. Acta Armamentarii, 2026, 47(1): 250527. DOI： 10.12382/bgxb.2025.0527.

摘要

在现代信息化战争与复杂民用场景（如城市峡谷、隧道、大型中空建筑）下，无人车面临全球卫星导航系统信号遮挡、惯性导航系统误差累积、视觉同步定位与建图受光照/纹理影响等问题，导致定位精度退化与连续性中断，严重制约其在侦察监视、物资运输、应急救援等任务中的效能。针对该问题，提出面向无人车跨场景机动的多源融合定位技术。以因子图优化为核心框架，融合视觉-惯性里程计（Visual-Inertial Odometry

VIO）与实时动态差分定位（Real-Time Kinematic

RTK）:VIO通过基于畸变像素映射表的直线特征提取提升纹理稀疏场景鲁棒性，同时辅助RTK进行卫星观测值预处理（周跳探测）与带航位推算的部分模糊度解算；RTK则提供全局无偏基准，修正VIO的累积漂移。实验结果表明：系统在室外开阔场景实现厘米级定位精度；城市峡谷场景（实验1）RTK固定率达90. 6%，半/全遮挡场景（实验2）固定率达75. 9%，且平均绝对误差、均方根误差均优于FixPosition、司南RTK等商用/开源方案。研究成果可为军事无人车跨场景作战与民用复杂城市场景无人驾驶、应急救援提供高精度定位支撑。

Abstract

In modern informationized warfare and complex civilian scenarios (e. g.

urban canyons

tunnels

large hollow buildings)

the autonomous vehicles face issues such as global navigation satellite system (GNSS ) signal blockage

inertial navigation system (INS ) error accumulation

and visual simultaneous localization and mapping (V-SLAM) being affected by illumination/texture

which result in the degradation of positioning accuracy and the interruption of continuity

thus severely restricting their effectiveness in tasks like reconnaissance

material transportation and emergency rescue. To address this problem

this paper proposes a multi-source fusion positioning technology for the cross-scenario maneuvering of autonomous vehicles. Methodologically

with factor graph optimization as the core framework

the technology integrates visual-inertial odometry (VIO ) and real-time kinematic (RTK ) positioning. VIO enhances the robustness in texture-sparse scenarios through line feature extraction based on distorted pixel mapping tables

and assists RTK in GNSS observation preprocessing (cycle slip detection) and partial ambiguity resolution with dead reckoning. RTK provides a global unbiased reference to correct VIO's cumulative drift. Experimental results show that the Technology achieves centimeter-level positioning accuracy in outdoor open scenarios. The RTK fixing rate reaches 90. 6% in urban canyon scenarios (Experiment 1) and 75. 9% in semi/full occlusion scenarios (Experiment 2)

and the mean absolute error (MAE ) and root mean square error (RMSE ) are superior to those of commercial/open-source solutions such as FixPosition and Sino RTK. The research results provide high-precision positioning support for cross-scenario operations of military autonomous vehicles

as well as autonomous driving and emergency rescue in complex urban civil scenarios.

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