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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:本文介绍了一个导航系统,使小型无人机车辆能够在没有GPS的叶子环境中自主导航,使用2D激光测距仪。导航框架包括使用姿势图优化,实时双层控制的实时车载运动估计和轨迹平滑。特别地,在卡尔曼滤波器中实现了车载实时运动估计,从激光范围查找器的扫描匹配和惯性测量单元的加速度测量中融合了从扫描匹配的平面速度测量。从实时自主导航的轨迹历史与观察到的特征一起被馈送到姿势图优化框架中。使用Graphslam技术优化滑动窗口中的姿势。使用商业自动驾驶仪稳定四轮压发电机的内环,而外环控制使用稳健的完美跟踪实现。导航系统的性能是关于森林中小规模无人机的成功自主导航。通过在具有Graphslam的后优化的轨迹上投影所有扫描测量来实现室内和室外场景中环境的一致映射。

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