Fault-Tolerant Multi-Modal Localization of Multi-Robots on Matrix Lie Groups
Mahboubeh Zarei, Robin Chhabra
TL;DR
The work addresses robust, scalable localization for cooperative multi-robot systems operating on matrix Lie groups, where accurate pose, velocity, and bias propagation is essential in GPS-denied environments. It introduces a set of stochastic operations on Lie groups—inverse, composition, difference, averaging, fusion, and constrained fusion—and integrates them into an EKF framework running on $\mathbb{SE}_2(3) \times \mathbb{R}^3 \times \mathbb{R}^3$ with multi-modal updates on $\mathbb{SE}(3) \times \mathbb{R}^3$. The framework supports three robot roles (leader, follower type 1, follower type 2) and uses fiducial markers (ArUco) for relative pose information, coupled with a Mahalanobis-distance-based fault detection to ensure measurement reliability. Experimental results with a network of wheeled robots demonstrate real-time performance, reliability, and scalability, validating accurate multi-robot localization under challenging conditions such as lighting changes, marker noise, and abrupt maneuvers. The approach offers a practical route to robust large-scale cooperative localization in indoor settings and lays groundwork for integrating additional sensors and broader robot platforms.
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.