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Autonomous navigation of UAV in forest

机译:无人机在森林中的自主导航

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摘要

This paper presents a navigation system that enables small-scale unmanned aerial vehicles to navigate autonomously in foliage environment without GPS using a 2D laser range finder. The navigation framework consists of real-time onboard motion estimation and trajectory smoothing using pose graph optimization, real-time dual layer control. In particular, onboard real-time motion estimation is achieved in a Kalman filter, fusing the planar velocity measurement from scan matching of laser range finder and the acceleration measurement of inertial measurement unit. The trajectory histories from the real-time autonomous navigation together with the observed features are fed into a pose-graph optimization framework. Poses in a sliding window are optimized using GraphSLAM technique. The inner loop of a quadrotor is stabilized using a commercial autopilot while the outer loop control is implemented using robust perfect tracking. The performance of the navigation system is demonstrated on the successful autonomous navigation of a small-scale UAV in forest. Consistent mapping of the environment in indoor and outdoor scenarios are achieved by projecting all the scan measurement on the post-optimized trajectory with GraphSLAM.
机译:本文提出了一种导航系统,该系统可使小型无人飞行器在没有GPS的情况下使用2D激光测距仪在树叶环境中自主导航。导航框架包括使用姿态图优化,实时双层控制的实时机载运动估计和轨迹平滑。特别是,通过将激光测距仪的扫描匹配的平面速度测量结果与惯性测量单元的加速度测量结果融合在一起,在卡尔曼滤波器中实现了车载实时运动估计。来自实时自主导航的轨迹历史以及所观察到的特征被输入到姿态图优化框架中。使用GraphSLAM技术优化滑动窗口中的姿势。使用商用自动驾驶仪可以稳定四旋翼的内环,而使用鲁棒的完美跟踪实现外环控制。导航系统的性能在森林中小型无人机的成功自主导航上得到了证明。通过使用GraphSLAM将所有扫描测量投影在优化后的轨迹上,可以实现室内和室外场景中环境的一致映射。

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