[Paper Review] A partially decentralized EKF scheme for cooperative localization over unreliable communication links.

Solmaz S. Kia, Sonia Martı́nez|arXiv (Cornell University)|Aug 1, 2016

Indoor and Outdoor Localization Technologies18 references3 citations

TL;DR

This paper proposes a partially decentralized Extended Kalman Filter (EKF) for cooperative localization in multi-robot systems with limited onboard resources. Robots maintain individual state estimates and only communicate with a central unit during measurement updates, reducing per-robot computational cost to O(1) in team size. The scheme ensures minimum-variance estimates under occasional communication dropouts in linear systems and provides suboptimal but stable solutions in nonlinear cases.

ABSTRACT

This paper reports a partially decentralized implementation of an Extended Kalman filter for the cooperative localization of a team of mobile robots with limited onboard resources. Unlike a fully centralized scheme that requires, at each timestep, information from the entire team to be gathered together and be processed by a single device, our algorithm only requires that the robots communicate with a central command unit at the time of a measurement update. Every robot only needs to propagate and update its own state estimate, while the central command unit maintains track of cross-covariances. Therefore, the computational and storage cost per robot in terms of the size of the team is of order O(1). Moreover, when the system model is linear the algorithm is robust to occasional in-network communication link failures while the updated estimates of the robots receiving the update message are of minimum variance at that given timestep. For problems with nonlinear robot models, our algorithm under message drop-out provides a suboptimal solutions because of the linearization approximation similar to the Extended Kalman filter model. We demonstrate the performance of the algorithm in simulation.

Motivation & Objective

• Address the high computational and communication burden of fully centralized EKF schemes in multi-robot cooperative localization.
• Reduce per-robot computational and storage costs to O(1) with respect to team size.
• Enable robust localization under unreliable communication links, particularly in linear system models.
• Provide a scalable solution for resource-constrained mobile robots in cooperative localization tasks.
• Maintain estimation accuracy despite intermittent message dropouts through a centralized cross-covariance management approach.

Proposed method

• Implement a partially decentralized EKF where each robot independently propagates and updates its own state estimate.
• Use a central command unit to maintain and update cross-covariances between all robots, reducing per-robot computation.
• Limit full communication to measurement update instants, minimizing bandwidth and processing load on individual robots.
• Apply linearization via the Extended Kalman Filter framework for nonlinear system models.
• Ensure that updated estimates remain minimum-variance under message dropouts when the system model is linear.
• Use a centralized fusion mechanism to reconcile robot state estimates while preserving estimation optimality in the linear case.

Experimental results

Research questions

• RQ1 Can a partially decentralized EKF reduce per-robot computational and storage costs to O(1) in team size while maintaining estimation accuracy?
• RQ2 How does the algorithm perform under intermittent communication link failures, especially in linear system models?
• RQ3 What is the impact of linearization errors on estimation performance in nonlinear robot motion and measurement models?
• RQ4 Can the central command unit maintain cross-covariance information to ensure consistent state estimates across the team?
• RQ5 Is the proposed scheme robust to message dropouts while still providing minimum-variance estimates in linear systems?

Key findings

• The algorithm achieves O(1) computational and storage cost per robot with respect to team size, significantly reducing resource demands.
• In linear system models, the algorithm provides minimum-variance estimates even under occasional communication link failures.
• For nonlinear models, the algorithm yields suboptimal solutions due to linearization errors, similar to standard EKF.
• The central command unit successfully maintains cross-covariance information, enabling consistent state estimation across the robot team.
• Simulation results demonstrate stable and accurate cooperative localization performance under realistic communication constraints.
• The scheme is scalable and practical for deployment on mobile robots with limited onboard processing and memory resources.