arXiv 2505.019562025-05-13cs.ROcs.AI

SafeNav：在GPS拒止环境中使用基于地标定位的安全路径导航

SafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment

Department of Computer Science（计算机科学系）；Missouri University of Science and Technology（密苏里科学与技术大学）

Ganesh Sapkota, Sanjay Madria

备注：: 10 pages, conference paper. arXiv admin note: text overlap with arXiv:2402.14280

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AI总结：

针对GPS拒止战场环境，本文提出结合地标定位、战场运动模型与EKF的LanBLoc-BMM，并基于RAw-RRT*构建两种SafeNav方法，实现更精确、低风险且高效的安全导航。

摘要：

在战场环境中，对手经常干扰GPS信号，因此需要替代性的定位与导航方法。传统的基于视觉的方法，如同步定位与地图构建（SLAM）和视觉里程计（VO），涉及复杂的传感器融合和较高的计算需求；而DV-HOP等无距离方法在稀疏、动态网络中面临精度和稳定性挑战。本文提出LanBLoc-BMM，这是一种使用基于地标定位（LanBLoc）并结合战场专用运动模型（BMM）与扩展卡尔曼滤波（EKF）的导航方法。研究将其性能与三种集成BMM和贝叶斯滤波器的先进视觉定位算法进行基准比较，并在合成轨迹数据集和真实模拟轨迹数据集上，使用平均位移误差（ADE）、最终位移误差（FDE）以及新提出的平均加权风险分数（AWRS）等指标进行评估。LanBLoc-BMM（采用EKF）在真实模拟数据集上的ADE、FDE和AWRS均表现出优越性能。此外，通过将LanBLoc-BMM（EKF）与用于避障和最小化风险暴露的新型风险感知RRT*（RAw-RRT*）算法相结合，本文还提出了SafeNav-CHull和SafeNav-Centroid两种安全导航方法。战场场景中的仿真结果表明，SafeNav-Centroid在精度、风险暴露和轨迹效率方面表现优异，而SafeNav-CHull则具有更出色的计算速度。

英文摘要：

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement Error (ADE), Final Displacement Error (FDE), and a newly introduced Average Weighted Risk Score (AWRS). LanBLoc-BMM (with EKF) demonstrates superior performance in ADE, FDE, and AWRS on real-imitated datasets. Additionally, two safe navigation methods, SafeNav-CHull and SafeNav-Centroid, are introduced by integrating LanBLoc-BMM(EKF) with a novel Risk-Aware RRT* (RAw-RRT*) algorithm for obstacle avoidance and risk exposure minimization. Simulation results in battlefield scenarios indicate SafeNav-Centroid excels in accuracy, risk exposure, and trajectory efficiency, while SafeNav-CHull provides superior computational speed.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。