The subject matter described herein includes a modular and extensible approach to integrate noisy measurements from multiple heterogeneous sensors that yield either absolute or relative observations at different and varying time intervals, and to provide smooth and globally consistent estimates of position in real time for autonomous flight. We describe the development of the algorithms and software architecture for a new 1.9 kg MAV platform equipped with an IMU, laser scanner, stereo cameras, pressure altimeter, magnetometer, and a GPS receiver, in which the state estimation and control are performed onboard on an Intel NUC 3rd generation i3 processor. We illustrate the robustness of our framework in large-scale, indoor-outdoor autonomous aerial navigation experiments involving traversals of over 440 meters at average speeds of 1.5 m/s with winds around 10 mph while entering and exiting buildings.
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机译:本文描述的主题包括模块化和可扩展的方法,以集成来自多个异类传感器的噪声测量,这些异类传感器在不同和变化的时间间隔上产生绝对或相对观测值,并为自动飞行实时提供平滑且全局一致的位置估计。我们描述了一种新的1.9千克MAV平台的算法和软件体系结构的开发,该平台配备了IMU,激光扫描仪,立体摄像机,压力高度计,磁力计和GPS接收器,其中状态估计和控制在机上进行英特尔NUC 3 rd Sup>代i3处理器。我们在大型室内-室外自主空中航行实验中说明了我们框架的鲁棒性,该实验涉及进出建筑物时以1.5 m / s的平均速度以每小时10 mph的风速穿越440米以上。
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