SafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment
This addresses the problem of reliable navigation for military or emergency responders in adversarial settings where GPS is unavailable, though it appears incremental as it builds on existing localization and path planning techniques.
The paper tackles safe path navigation in GPS-denied battlefield environments by proposing LanBLoc-BMM, a landmark-based localization method combined with a motion model and Extended Kalman Filter, which outperforms visual localization algorithms in metrics like Average Displacement Error and Final Displacement Error on real-imitated datasets, and introduces SafeNav methods that integrate this with a risk-aware algorithm for obstacle avoidance.
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.