Monocular Visual Mapping for Obstacle Avoidance on UAVs
Monocular Visual Mapping for Obstacle Avoidance on UAVs
复制标题
用于无人机避障的单目视觉测绘
DOI：
10.1007/s10846-013-9967-7
复制
发表时间：
2013
影响因子：
3.3
通讯作者：
Eric N. Johnson
中科院分区：
文献类型：
--
作者：
Daniel Magree;J. G. Mooney;Eric N. Johnson
无人驾驶飞行器需要对其周围环境有足够的了解，以便在非常接近障碍物的情况下飞行。无人机还具有严格的有效载荷和功率限制，这限制了可用于收集这些信息的传感器的数量和种类。因此，期望使UAV能够使用通用且轻量的传感器系统来收集关于潜在障碍物或感兴趣的地标的信息。本文提出了一种利用单目摄像机进行快速地形测绘的方法。从相机图像中提取特征，并用于更新序列扩展卡尔曼滤波器。特征位置以逆深度进行参数化，以实现快速深度收敛。融合的功能被添加到一个持久的地形图，可用于避障和额外的车辆指导。仿真结果，结果记录的飞行试验数据，飞行试验结果，以验证算法。
An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.