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.

翻译：本文针对机器人团队中的去中心化协同状态估计问题展开研究。特别地，本文考虑了个体机器人估计相似物理量（例如彼此之间的相对位置）的问题。提出采用伪测量作为建模机器人状态估计之间关系的手段，并证明其为解决去中心化状态估计问题的一种可行途径。此外，该公式可自然推导出一种通用可观测性检验方法，该方法能同时考虑机器人自身传感器采集的测量值以及团队内部的通信结构。最后，提出输入预积分作为机器人之间共享里程计信息的通信高效方式，且整套理论适用于向量空间和李群状态定义。为克服预积分协方差信息通信的需求，提出一种深度自编码器，通过输入重构协方差信息，从而进一步降低通信需求。所提出的框架在三个不同仿真问题及一项涉及三架四旋翼飞行器的实验中进行了评估。