無人機在現今產業發展快速，因其相較於需人員搭載的飛行器有更低的製造成本、更高的機動性而且減少了駕駛人員傷亡的風險而大量應用於過往需要人力的工作上。無人機發展初期主要應用於軍事用途上，但隨著技術逐漸商用化無人機逐漸在民生用途上取得大量的發展，包含在工業、農業、電影拍攝甚至是競技娛樂都出現了無人機的應用技術。除了無人機硬體本身的發展外，影像處理的技術發展使無人機能在更多應用場景發揮價值，尤其是平面視覺的影像處理技術使無人機能夠以平面視覺的相機進行更多的任務，特別是對於重量有限制而無法搭載大量感測器的微型無人機。
對於微型無人機來說平面相機、GPS、指北針與高度計是常備的感測器，因此本研究對於偵查區域內目標物件定位任務以微型無人機常備的感測器發展三項定位策略來達成不同任務環境下的目標偵查與定位。其中，多點平均定位策略以單目視覺定位模組為主，搭配平面視覺影像及感測器數據來達成對地上目標物件的定位，並利用了無人機執行任務的連續性對感測器資料進行校正進而提升定位結果的可靠度。為了降低感測器的依賴程度，本研究以三角計算的方式發展三角測量定位策略，成功降低感測器的依賴度以及高度對於定位準確度的影響。影像比對定位策略則是以特徵比對技術為基礎來達成純影像的定位任務，使無人機在感測器失效的任務環境下仍能夠達成定位任務。

With the rapid development of unmanned aerial vehicle technology and it’s high mobility, low risk for drone pilot.Unmanned Aerial Vehicle have been used in a variety of applications.In early stage, UAV was mainly used for military purposes.But, with UAV technology became more and more prevalent, UAV widely applied on Manufacturing industry, agriculture and film industry.Beside the UAV technology, the development of image processing also improve development of UAV application.And 2D-vision-based image processing was important especially for micro UAV because of it’s weight limit.
For micro UAV, the commonly equipped sensors are a camera, GPS, compass and altimeter. Therefore, this research develop three geolocation strategies using the sensors commonly found on micro drones to achieve target detection and positioning in different mission environments. Among them, the multi-point averaging geolocation strategy focuses on the monocular visual positioning module, using plane visual images and sensor data to locate ground target.This strategy also calibrate the sensor data to improve the reliability of the positioning results. To reduce sensor dependency, a triangulation geolocation strategy was developed using trigonometric calculations, successfully reducing the reliance on sensors and mitigating the impact of altitude on positioning accuracy. The image matching geolocation strategy is based on feature matching techniques to achieve pure imagebased positioning tasks, enabling drones to perform positioning tasks even in mission environments where sensors may fail.

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