Cooperative Localization under Limited Connectivity (1904.13074v2)

Abstract: We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown inter-agent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic~experiment.

• The paper presents two decentralized algorithms, DMV and ECMV, to address cooperative localization under limited connectivity by implicitly accounting for inter-agent state estimate correlations without high communication costs.
• The Estimated Cross-covariance Minimum Variance (ECMV) method achieves higher localization accuracy than the Discorrelated Minimum Variance (DMV) method through cross-covariance estimation.
• These algorithms enable consistent, decentralized localization for autonomous systems in connectivity-constrained environments like underwater navigation or rescue missions.

Cooperative Localization under Limited Connectivity

In the context of autonomous multi-agent systems, particularly in scenarios where maintaining persistent network connectivity is challenging, cooperative localization (CL) offers a promising approach to enhance the localization accuracy of mobile agents such as robots and unmanned vehicles. The paper "Cooperative Localization under Limited Connectivity" by Jianan Zhu and Solmaz S. Kia presents two decentralized algorithms aimed at addressing the limitations posed by intermittent connectivity while preserving the consistency of localization estimates.

Technical Overview

The primary challenge in decentralized CL systems lies in processing inter-agent relative measurements efficiently, without incurring high communication costs associated with maintaining explicit state estimate correlations, which traditional methods require. The algorithms introduced in this paper provide innovative solutions to this problem by implicitly accounting for these correlations, thereby reducing communication overhead.

1. Discorrelated Minimum Variance (DMV) Method: The DMV method circumvents the need for inter-agent state estimate correlations by employing an upper bound on the joint covariance matrix of the agents involved in the relative measurement update. This approach parallels covariance intersection (CIF) methods but takes a direct route by not requiring reconstruction of a state estimate from relative pose measurements. By finding a balance within the estimated bounds, the DMV method ensures that the consistency of the agent's local state estimate is preserved after the update.
2. Estimated Cross-covariance Minimum Variance (ECMV) Method: The ECMV method takes a different approach by seeking to estimate the unknown inter-agent cross-covariance matrix. Using an optimization framework, the method aims to construct this matrix to improve localization accuracy. The paper provides a rigorous consistency analysis of the ECMV method, showing its efficacy in achieving more accurate localization than DMV, albeit at a higher computational cost.

Results and Implications

The paper presents both simulation and experimental results which demonstrate the efficacy and performance advantages of the proposed methods. For instance, PECMV surpasses DMV in terms of localization accuracy, confirming the effectiveness of estimating cross-covariance matrices. Despite the increased computational load, PECMV offers significant improvements over traditional methods, showcasing a distinct improvement compared to naive CL approach, which disregards the correlations.

Practical and Theoretical Implications

The algorithms developed in this paper have wide-ranging implications for future deployments of autonomous systems, especially in environments where persistent connectivity cannot be guaranteed. They provide a decentralized framework that allows agents to locally improve their position estimates without needing comprehensive network-wide synchronization.

• Practical Applications: These methods could be directly applicable to scenarios such as underwater vehicle navigation, smart car localization, and rescue missions in environments where GPS is unreliable or unavailable.
• Theoretical Contributions: The paper advances the field by introducing computationally feasible mechanisms to implicitly account for correlations in decentralized systems, which has been a significant hurdle for deploying CL in real-world applications.

Speculations on Future AI Developments

The methods presented suggest potential pathways for enhancing collaboration among AI entities in settings where direct communication channels are unreliable. As AI systems continue to evolve, incorporating such decentralized strategies could lead to more robust and adaptive multi-agent systems, with implications for swarm robotics and distributed sensing networks.

Future research may focus on further reducing computational costs and improving the scalability of these methods to accommodate larger numbers of agents, potentially incorporating more sophisticated techniques such as machine learning for dynamic estimation of cross-covariance matrices.

In conclusion, the paper makes substantial contributions towards overcoming connectivity limitations in multi-agent systems, offering decentralized yet consistent localization strategies that are particularly pertinent for advancing the capabilities of autonomous systems operating in complex and connectivity-constrained environments.