Decentralized State Estimation: An Approach using Pseudomeasurements and Preintegration

TL;DR

This paper presents a decentralized state estimation framework for robotic teams using pseudomeasurements and preintegration, enabling efficient, collaborative localization with reduced communication overhead.

Contribution

It introduces pseudomeasurements for modeling inter-robot relationships and proposes input preintegration with autoencoder-based covariance reconstruction for communication efficiency.

Findings

Effective decentralized estimation demonstrated in simulations and quadcopter experiments.

The approach provides a general observability test considering sensor and communication data.

Autoencoder reduces communication load by reconstructing covariance information.

Abstract

This paper addresses the problem of decentralized, collaborative state estimation in robotic teams. In particular, this paper considers problems where individual robots estimate similar physical quantities, such as each other's position relative to themselves. The use of pseudomeasurements is introduced as a means of modelling such relationships between robots' state estimates, and is shown to be a tractable way to approach the decentralized state estimation problem. Moreover, this formulation easily leads to a general-purpose observability test that simultaneously accounts for measurements that robots collect from their own sensors, as well as the communication structure within the team. Finally, input preintegration is proposed as a communication-efficient way of sharing odometry information between robots, and the entire theory is appropriate for both vector-space and Lie-group state definitions. To overcome the need for communicating preintegrated-covariance information, a deep autoencoder is proposed that reconstructs the covariance information from the inputs, hence further reducing the communication requirements. The proposed framework is evaluated on three different simulated problems, and one experiment involving three quadcopters.