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Binocular Vision-Based Autonomous Path Planning for UAVs in Unknown Outdoor Scenes

机译:基于双目视觉的未知室外场景的无人机自主路径规划

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It is a classic task for Unmanned Aerial Vehicles (UAVs) to accomplish autonomous scene perception and path planning in unknown 3-D outdoor environments. This paper investigates the problems of obstacle avoidance and path planning using binocular vision system. During the UAV's flight, the binocular vision sensor is used to obtain the local environment information in real time, and the distribution of obstacles in the environment can also be analyzed with the depth images acquired by the binocular vision. Inspired by the idea of dynamic window algorithm, a 3-D path planning algorithm is proposed to convert the global path to the combination of a group of local paths by using a series of 3-D models of predefined local paths. According to the screening algorithm for the passable candidate paths, the UAV will select the optimal one to guide its flight. A series of experiments are conducted by using a quadrotor platform DJI M100 and experimental results show the validity of the proposed approach.
机译:对于无人机而言,在未知的3D室外环境中完成自主的场景感知和路径规划是一项经典任务。本文研究了使用双目视觉系统的避障和路径规划问题。在无人机飞行过程中,双目视觉传感器用于实时获取本地环境信息,还可以利用双目视觉获取的深度图像分析环境中的障碍物分布。受动态窗口算法思想的启发,提出了一种3-D路径规划算法,该算法通过使用一系列预定义的局部路径的3D模型将全局路径转换为一组局部路径的组合。根据可通过的候选路径的筛选算法,无人机将选择最佳的一种来指导其飞行。使用四旋翼平台DJI M100进行了一系列实验,实验结果表明了该方法的有效性。

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