Fault-Tolerant Multi-Modal Localization of Multi-Robots on Matrix Lie Groups

TL;DR

This paper introduces a fault-tolerant, multi-modal localization framework for multi-robot systems on matrix Lie groups, combining sensor data and fault detection to improve real-time accuracy, scalability, and reliability.

Contribution

It presents novel stochastic operations on Lie groups for sensor fusion, integrating multiple measurement modalities within an EKF framework for scalable multi-robot localization.

Findings

Demonstrates real-time performance on wheeled robots with inertial and visual sensors.

Shows improved reliability and scalability over existing methods.

Validates effectiveness through experiments with ArUco markers and inter-robot communication.

Abstract

Consistent localization of cooperative multi-robot systems during navigation presents substantial challenges. This paper proposes a fault-tolerant, multi-modal localization framework for multi-robot systems on matrix Lie groups. We introduce novel stochastic operations to perform composition, differencing, inversion, averaging, and fusion of correlated and non-correlated estimates on Lie groups, enabling pseudo-pose construction for filter updates. The method integrates a combination of proprioceptive and exteroceptive measurements from inertial, velocity, and pose (pseudo-pose) sensors on each robot in an Extended Kalman Filter (EKF) framework. The prediction step is conducted on the Lie group $\mathbb{SE}_2(3) \times \mathbb{R}^3 \times \mathbb{R}^3$, where each robot's pose, velocity, and inertial measurement biases are propagated. The proposed framework uses body velocity, relative pose measurements from fiducial markers, and inter-robot communication to provide scalable EKF update across the network on the Lie group $\mathbb{SE}(3) \times \mathbb{R}^3$. A fault detection module is implemented, allowing the integration of only reliable pseudo-pose measurements from fiducial markers. We demonstrate the effectiveness of the method through experiments with a network of wheeled mobile robots equipped with inertial measurement units, wheel odometry, and ArUco markers. The comparison results highlight the proposed method's real-time performance, superior efficiency, reliability, and scalability in multi-robot localization, making it well-suited for large-scale robotic systems.