Distributed and Consistent Multi-Robot Visual-Inertial-Ranging Odometry on Lie Groups

2026-02-22

AI Summary

This work addresses the challenge of drift-prone visual-inertial odometry (VIO) in GPS-denied environments for multi-robot systems, where existing ultra-wideband (UWB)-assisted approaches are typically limited to single robots and rely on pre-calibrated anchor nodes, compromising robustness. To overcome these limitations, we propose a distributed collaborative visual-inertial-ranging odometry framework (DC-VIRO), which, for the first time, tightly integrates VIO with online self-calibration of UWB anchors in multi-robot settings. By incorporating anchor positions into the system state and leveraging shared UWB measurements to impose geometric constraints, DC-VIRO employs right-invariant error modeling on Lie groups and distributed optimization to ensure observability and estimation consistency. Experimental results demonstrate that DC-VIRO significantly improves localization accuracy and robustness while successfully achieving distributed self-calibration of UWB anchors.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Reliable localization is a fundamental requirement for multi-robot systems operating in GPS-denied environments. Visual-inertial odometry (VIO) provides lightweight and accurate motion estimation but suffers from cumulative drift in the absence of global references. Ultra-wideband (UWB) ranging offers complementary global observations, yet most existing UWB-aided VIO methods are designed for single-robot scenarios and rely on pre-calibrated anchors, which limits their robustness in practice. This paper proposes a distributed collaborative visual-inertial-ranging odometry (DC-VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots. Anchor positions are explicitly included in the system state to address calibration uncertainty, while shared anchor observations are exploited through inter-robot communication to provide additional geometric constraints. By leveraging a right-invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO, ensuring estimator consistency. Simulation results with multiple robots demonstrate that DC-VIRO significantly improves localization accuracy and robustness, while simultaneously enabling anchor self-calibration in distributed settings.

Problem

• multi-robot localization
• visual-inertial odometry
• UWB ranging
• anchor calibration
• GPS-denied environments

Innovation

• distributed multi-robot odometry
• visual-inertial-ranging fusion
• UWB self-calibration
• Lie group estimation
• right-invariant error