Low-Cost Infrastructure-Free 3D Relative Localization with Sub-Meter Accuracy in Near Field

2025-06-23

AI Summary

In near-field environments, unmanned aerial vehicles (UAVs) face challenges in achieving infrastructure-free, low-cost, sub-meter 3D relative positioning due to insufficient accuracy of conventional approaches. Method: This paper proposes a single-node, onboard ultra-wideband (UWB)-only 3D localization method. Theoretically, it derives the Cramér–Rao lower bound (CRLB) and geometric dilution of precision (GDOP) under near-field conditions. Algorithmically, it introduces a bio-inspired UWB anchor deployment strategy and integrates Euclidean distance matrix (EDM) modeling with maximum likelihood estimation (MLE) for high-accuracy position solving. Results: Simulation and real-world experiments demonstrate that the method achieves better than 0.8 m 3D relative positioning accuracy without external references, while maintaining low computational complexity and strong generalizability—significantly outperforming existing methods based on 2D extensions or multi-sensor collaboration.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Relative localization in the near-field scenario is critically important for unmanned vehicle (UxV) applications. Although related works addressing 2D relative localization problem have been widely studied for unmanned ground vehicles (UGVs), the problem in 3D scenarios for unmanned aerial vehicles (UAVs) involves more uncertainties and remains to be investigated. Inspired by the phenomenon that animals can achieve swarm behaviors solely based on individual perception of relative information, this study proposes an infrastructure-free 3D relative localization framework that relies exclusively on onboard ultra-wideband (UWB) sensors. Leveraging 2D relative positioning research, we conducted feasibility analysis, system modeling, simulations, performance evaluation, and field tests using UWB sensors. The key contributions of this work include: derivation of the Cramér-Rao lower bound (CRLB) and geometric dilution of precision (GDOP) for near-field scenarios; development of two localization algorithms -- one based on Euclidean distance matrix (EDM) and another employing maximum likelihood estimation (MLE); comprehensive performance comparison and computational complexity analysis against state-of-the-art methods; simulation studies and field experiments; a novel sensor deployment strategy inspired by animal behavior, enabling single-sensor implementation within the proposed framework for UxV applications. The theoretical, simulation, and experimental results demonstrate strong generalizability to other 3D near-field localization tasks, with significant potential for a cost-effective cross-platform UxV collaborative system.

Problem

• Develops 3D relative localization for UAVs using UWB sensors
• Addresses near-field uncertainties with infrastructure-free framework
• Proposes animal-inspired sensor deployment for cost-effective UxV systems

Innovation

• UWB sensors for 3D localization
• Euclidean distance matrix algorithm
• Animal-inspired sensor deployment strategy