为了确保植保无人机在飞行作业过程中的安全,要求植保无人机具有自动避障能力,为此提出了基于结构光视觉的障碍物检测方法。为提高障碍物检测的实时性,重点研究了基于嵌入式平台的植保无人机障碍物检测系统,通过将障碍物图像处理算法的并行计算映射到GPU硬件资源上完成,大大提高了算法的运行效率。实验表明,在保证障碍物轮廓线完整的前提下,通过对比CPU和CPU-GPU实现处理算法,障碍物检测系统获得了约46.15的加速比,采集及处理时间约为48.985 ms。该系统具有处理效果明显与实时性好等优点,为植保无人机的实时障碍物检测和进一步实现自动避障奠定了基础。

In order to ensure the safety of plant protection unmanned aerial vehicles (UAVs), it was required to have the ability of the automatic obstacle avoidance. So a new obstacle detection method based on structured light vision was proposed. In order to improve the real-time performance of obstacle detection, based on the analysis of the latest general processor unit (GPU), the obstacle detection system of plant protection UAVs based on embedded platform was studied mainly. By mapping the obstacle image processing algorithm to GPU hardware resources to complete the parallel computation, the efficiency of the algorithm was greatly improved. Experiments show that the obstacle detection system based on embedded CPU-GPU achieves a speedup rate of about 46.15 by comparing the processing algorithms of CPU and CPU-GPU on the premise of ensuring the complete outline of the obstacle, and time-consuming of the acquisition and processing is about 48.985 ms. The system has the advantages of obvious effect of processing and good real-time performance, which lays the foundation for real-time obstacle detection and further obstacle avoidance of plant protection UAVs.