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Cooperative visual-inertial sensor fusion: Fundamental equations

机译:协作式视觉惯性传感器融合:基本方程式

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This paper provides a new theoretical and basic result in the framework of cooperative visual-inertial sensor fusion. Specifically, the case of two aerial vehicles is investigated. Each vehicle is equipped with inertial sensors (accelerometer and gyroscope) and with a monocular camera. By using the monocular camera, each vehicle can observe the other vehicle. No additional camera observations (e.g., of external point features in the environment) are considered. First, the entire observable state is analytically derived. This state includes the relative position between the two aerial vehicles (which includes the absolute scale), the relative velocity and the three Euler angles that express the rotation between the two vehicle frames. Then, the basic equations that describe this system are analytically obtained. In other words, both the dynamics of the observable state and all the camera observations are expressed only in terms of the components of the observable state and in terms of the inertial measurements. These are the fundamental equations that fully characterize the problem of fusing visual and inertial data in the cooperative case. The last part of the paper describes the use of these equations to achieve the state estimation through an EKF. In particular, a simple manner to limit communication among the vehicles is discussed. Results obtained through simulations show the performance of the proposed solution, and in particular how it is affected by limiting the communication between the two vehicles.
机译:本文在视觉惯性传感器协同融合的框架下提供了新的理论和基础成果。具体地,研究了两个飞行器的情况。每辆车都配备有惯性传感器(加速度计和陀螺仪)以及单眼相机。通过使用单眼相机,每辆车都可以观察另一辆车。没有考虑其他相机观测结果(例如,环境中的外部点特征)。首先,通过分析得出整个可观察状态。该状态包括两个飞行器之间的相对位置(包括绝对比例),相对速度和三个欧拉角,三个欧拉角表示两个车架之间的旋转。然后,获得描述该系统的基本方程式。换句话说,可观察状态的动力学和所有摄像机观察都仅根据可观察状态的分量和惯性测量来表示。这些是基本方程,完全可以描述在协作情况下融合视觉数据和惯性数据的问题。本文的最后一部分描述了这些方程的使用,以通过EKF实现状态估计。特别地,讨论了限制车辆之间的通信的简单方式。通过仿真获得的结果表明了所提出解决方案的性能,尤其是如何通过限制两辆车之间的通信来影响解决方案。

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