We propose a system solution to achieve data-efficient, decentralized state estimation for a team of flying robots using thermal images and inertial measurements. Each robot can fly independently, and exchange data when possible to refine its state estimate. Our system front-end applies an online photometric calibration to refine the thermal images so as to enhance feature tracking and place recognition. Our system back-end uses a covariance-intersection fusion strategy to neglect the cross-correlation between agents so as to lower memory usage and computational cost. The communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to construct a request-response policy that requires low bandwidth usage. We test our collaborative method on both synthetic and real-world data. Our results show that the proposed method improves by up to 46 % trajectory estimation with respect to an individual-agent approach, while reducing up to 89 % the communication exchange. Datasets and code are released to the public, extending the already-public JPL xVIO library.

翻译：我们提出一个系统解决方案,以实现使用热图像和惯性测量的飞行机器人团队的数据高效、分散的状态估算。 每个机器人都可以独立飞行,并尽可能交换数据以完善其状态估算。 我们的系统前端应用在线光度校准来改进热图像,以加强特征跟踪和位置识别。 我们的系统后端使用共变量交错组合战略,忽视代理人之间的交叉关系,以降低记忆使用和计算成本。 通信管道使用本地集成描述器(VLAD)的矢量来构建一个需要低带宽的使用的要求-反应政策。 我们测试我们的合成数据和现实世界数据合作方法。 我们的结果显示,拟议方法在单个试剂方法方面改进了高达46%的轨迹估计,同时将通信交换减少高达89 %。 数据集和代码向公众发布,扩大了已经公开的 JPL xVIO 图书馆。