去中心化状态估计：一种使用伪测量与预积分的方法
Decentralized State Estimation: An Approach using Pseudomeasurements and Preintegration
AI总结：
本文提出基于伪测量与输入预积分的去中心化协作状态估计框架，并利用深度自编码器重建协方差信息以降低通信需求，适用于向量空间和李群状态。
摘要：
本文研究机器人团队中的去中心化协作状态估计问题。特别地，本文考虑的是单个机器人估计相似物理量的问题，例如彼此相对于自身的位置。引入伪测量作为建模机器人状态估计之间此类关系的一种手段，并证明它是处理去中心化状态估计问题的一种可行方法。此外，该表述很容易导出一个通用的可观测性检验方法，该方法同时考虑机器人从自身传感器收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一种通信高效的方式，用于在机器人之间共享里程计信息，并且整个理论既适用于向量空间状态定义，也适用于李群状态定义。为了克服需要通信预积分协方差信息的问题，提出了一种深度自编码器，该自编码器从输入中重建协方差信息，从而进一步降低通信需求。所提出的框架在三个不同的仿真问题以及一个涉及三架四旋翼飞行器的实验上进行了评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
