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Abstract：
Motion estimation is a major issue in applications of Unmanned Aerial Vehicles (UAVs). This paper proposes an entire solution to solve this issue using information from an Inertial Measurement Unit (IMU) and a monocular camera. The solution includes two steps: visual location and multisensory data fusion. In this paper, attitude information provided by the IMU is used as parameters in Kalman equations, which are different from pure visual location methods. Then, the location of the system is obtained, and it will be utilized as the observation in data fusion. Considering the multiple updating frequencies of sensors and the delay of visual observation, a multi-rate delay-compensated optimal estimator based on the Kalman filter is presented, which could fuse the information and obtain the estimation of 3D positions as well as translational speed. Additionally, the estimator was modified to minimize the computational burden, so that it could run onboard in real time. The performance of the overall solution was assessed using field experiments on a quadrotor system, compared with the estimation results of some other methods as well as the ground truth data. The results illustrate the effectiveness of the proposed method.
摘要：
运动估计是无人机（UAV）应用中的主要问题。本文提出了一种使用来自惯性测量单元（IMU）和单目相机的信息来解决此问题的完整解决方案。该解决方案包括两个步骤：视觉定位和多感官数据融合。在本文中,IMU提供的姿态信息用作卡尔曼方程中的参数，这与纯视觉定位方法不同。然后，获得系统的位置，它将被用作数据融合中的观测。考虑到传感器的多个更新频率和视觉观察的延迟,提出了一种基于卡尔曼滤波器的多速率时延补偿最优估计器，它可以融合信息并获得3D位置的估计以及平移速度。此外,对估计器进行了修改，以最大限度地减少计算负担，这样它就可以在船上实时运行。使用四旋翼系统的现场实验评估了整体解决方案的性能，与其他一些方法的估计结果以及地面实况数据进行比较。实验结果表明了该方法的有效性。
