CORELS: A Cooperative Relative Localization System for Multi-agent Networks

Authors

Abstract

Reliable and accurate spatio-temporal information is of great importance for multi-agent networks. Cooperative relative localization technologies provide a promising paradigm for such information, especially in GNSS-denied or infrastructure-free scenarios without absolute position reference. In this paper, we develop a cooperative relative localization system (CORELS) to achieve high-precision and low-latency localization capability for distributed multi-agent networks. A backbone-listener scheme is proposed to enable large-scale agents to complete high-precision localization. Backbone agents are selected to reduce information loss caused by malformed topology, and meanwhile a large amount of listener agents complete position and orientation angle estimation simultaneously. Different from traditional triangulation localization methods, CORELS has no prerequisites for position-known base stations. The localization accuracy is improved by a back calibration algorithm which makes use of the measurements and localization results of agents‘ neighbours. The distributed localization capability is extend to relative dynamic scenes by a dead reckoning filtering scheme, where spatio-temporal cooperation are accomplished by information fusion of ranging, angle and inertial navigation measurements. Moreover, we implement CORELS on a low-cost hardware platform. Extensive simulation and real-world experiments demonstrate that the proposed system achieves decimeter-level relative localization, which is close to the theoretical Cramer–Rao lower bound limit.

Contents

Architecture

Simulation Results

Hardware Implementation in Static Scenes

Hardware Implementation in Dynamic Scenes

Architecture

The CORELS architecture
The Backbone-Listener architecture
The dead reckoning filtering scheme
llustration of the coordinate transformation

Simulation Results

Node Activation Strategies

SPEB comparison for different node activation strategies

Localizaition performance of the distributed relative localization algorithm for static scenes

RMSE distribution of TDoA-based algorithm RMSE distribution of TDoA-AoA-based algorithm RMSE distribution of the proposed algorithm
CDF of relative localization error

Localizaition performance of DRFS for dynamic scence

The GNA strategy changes the anchor configuration when topologically deformed The DRFS (in blue) significantly reduces error accumulation

The simulation platform is able to visually diplay the real-time localization results and RMSE

Display of the simulated data Display of the external input data

Hardware Implementation in Static Scenes

Illustrations of the hardware platform

The intelligent agent (vehicle) and its architecture diagram
The OptiTrack motion capture system Photo of the experiment in an actual scene

Rectangular placement

Ideal topology Estimated topology Localization results

Random placement

Ideal topology Estimated topology Localization results

Hardware Implementation in Dynamic Scenes

Translation and rotation invariance in relative localization

Translation and rotation do not affect topology estimation in relative localizaiton scenarios

Real-world experiments on our practical hardware platform

Extensive real-world experiments validate our system achieves decimeter-level relative localization accuracy

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