基于实时视觉传感器的无人机低空测绘特征检测技术实现

M. F. Misnan, N. M. Arshad, R. Shauri, Noor Hayati Abd Razak, N. M. Thamrin, S. F. Mahmud
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引用次数: 8

摘要

特征检测是自动驾驶汽车在农业领域获取参考或引导的一项重要任务。近年来,许多传感器被用于农田特征检测,如声纳传感器、激光测距仪、红外传感器,它们被用于高度估计、尺寸面积测量和路径跟踪,作为参考,用于创建虚拟农田地图,用于自动车辆导航过程。在无人驾驶车辆上实现了一种基于视觉的低空测绘传感器。以无人机为运输要素,要求以最小的计算量和轻量的设备实现实时性。实验装置利用模拟摄像机作为所需检测的前端传感器。模拟视觉传感器对实验表面进行扫描,用于特征检测应用的传感器精度分析。该处理技术通过中速16位处理器微控制器实现,避免了对高速处理器进行图像处理的要求。该视觉传感器将应用于无人机,用于现场测量的特征检测方案。所产生的合成图像与实际图像进行了比较,以便对基于视觉的传感器进行性能分析。所设计的技术给出了可接受的图像精度误差,足以用于无人机特征检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time vision based sensor implementation on unmanned aerial vehicle for features detection technique of low altitude mapping
Feature detection is an important task for an autonomous vehicle in getting reference or guide in a agriculture field. Recently, a lot of sensors were implemented for feature detection of a field such as sonar sensor, laser range finder, infrared sensor which it is used as height estimation, size area measurement and path tracking that act as reference for desired measurement in creating a virtual map of the agriculture field which can be used as autonomous vehicle navigation process. A vision based sensor is implemented on the unmanned area vehicle for low altitude mapping. Real-time process with minimum computational process and light weight devices is required and utilized as it used UAV as the transportation element. An experimental setup utilized an analog camera as the front end sensor for the desired detection. The experimental surface is scanned by the analog vision sensor for sensor accuracy analysis used for feature detection application. The process technique is implemented via the medium speed 16-bit processor microcontroller which avoids the requirement of high-speed processor to do image processing. The vision sensor will be utilized into an unmanned aerial vehicle used for feature detection scheme for field measurement. The composing image produced is shown for comparison with the actual image as for performance analysis of the vision based sensor. The designed technique gave acceptable error of image accuracy that adequate to use for features detection on unmanned aeria vehicle.
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