Radio-based Multi-Robot Odometry and Relative Localization

2025-09-30

AI Summary

To address the low accuracy and poor robustness of relative localization and odometry for heterogeneous multi-robot systems (UGVs/UAVs) in complex environments, this paper proposes a loosely coupled multi-sensor fusion framework integrating UWB ranging, millimeter-wave radar, IMU, and wheel encoders. We innovatively design a radar preprocessing module and a self-motion estimation module, formulate a nonlinear optimization problem on a factor graph, and solve it via Ceres Solver for pose-graph optimization. A Gazebo-based UWB simulation plugin is introduced to enhance data fidelity. Extensive evaluation on SITL simulations and real-world datasets demonstrates that our method achieves significantly higher relative pose estimation accuracy than classical closed-form solutions. The system is implemented in ROS 2, with all source code, datasets, and benchmarking tools publicly released. It supports seamless extension to SLAM applications and exhibits high reproducibility and practical potential.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Radio-based methods such as Ultra-Wideband (UWB) and RAdio Detection And Ranging (radar), which have traditionally seen limited adoption in robotics, are experiencing a boost in popularity thanks to their robustness to harsh environmental conditions and cluttered environments. This work proposes a multi-robot UGV-UAV localization system that leverages the two technologies with inexpensive and readily-available sensors, such as Inertial Measurement Units (IMUs) and wheel encoders, to estimate the relative position of an aerial robot with respect to a ground robot. The first stage of the system pipeline includes a nonlinear optimization framework to trilaterate the location of the aerial platform based on UWB range data, and a radar pre-processing module with loosely coupled ego-motion estimation which has been adapted for a multi-robot scenario. Then, the pre-processed radar data as well as the relative transformation are fed to a pose-graph optimization framework with odometry and inter-robot constraints. The system, implemented for the Robotic Operating System (ROS 2) with the Ceres optimizer, has been validated in Software-in-the-Loop (SITL) simulations and in a real-world dataset. The proposed relative localization module outperforms state-of-the-art closed-form methods which are less robust to noise. Our SITL environment includes a custom Gazebo plugin for generating realistic UWB measurements modeled after real data. Conveniently, the proposed factor graph formulation makes the system readily extensible to full Simultaneous Localization And Mapping (SLAM). Finally, all the code and experimental data is publicly available to support reproducibility and to serve as a common open dataset for benchmarking.

Problem

• Developing multi-robot UGV-UAV relative localization using UWB and radar
• Estimating aerial robot position relative to ground robot robustly
• Enhancing noise-resistant localization via optimization frameworks and sensors

Innovation

• UWB trilateration optimizes aerial robot positioning
• Radar preprocessing enables multi-robot ego-motion estimation
• Pose-graph fusion integrates odometry and inter-robot constraints