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

TL;DR

SafeNav introduces a landmark-based localization method combined with a battlefield-specific motion model and risk-aware navigation algorithms to enable safe, accurate path planning in GPS-denied battlefield environments.

Contribution

The paper presents LanBLoc-BMM, a novel localization approach, and SafeNav-CHull and SafeNav-Centroid, new safe navigation methods tailored for GPS-denied scenarios.

Findings

LanBLoc-BMM outperforms existing visual localization algorithms in ADE, FDE, and AWRS.

SafeNav-Centroid achieves high accuracy and low risk exposure in simulations.

SafeNav-CHull offers faster computation while maintaining acceptable accuracy.

Abstract

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.