無人機應用於室內停車場停車引導

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Title:	無人機應用於室內停車場停車引導
Authors:	許紫琳;Hsu, Tzu-Lin
Contributors:	電機工程學系
Keywords:	無人機;室內定位;路徑規劃;物件追蹤;特徵匹配;Drone;Indoor Positioning;Path Planning;Object Tracking;Feature Matching
Date:	2023-05-15
Issue Date:	2023-10-04 16:07:20 (UTC+8)
Publisher:	國立中央大學
Abstract:	汽車進入室內大型停車場，若無任何空車位指引機制，往往要花費許多時間在尋找空車位，若剩餘車位不多，很可能需要繞行多條沒有車位的路段才能找到空車位，既浪費時間，也浪費力氣。因此本論文旨在建立一套無人機之室內停車場引導系統。首先將停車場地圖建入系統，系統會對每一輛進入停車場欲停車的車輛自動選擇距離停車場入口最近的空車位，並規畫出到達停車場入口的最短路徑，無人機將依據此路徑為汽車做引導，引導完成後，無人機將拍攝汽車停放完成的影像回傳給系統更新車位狀態，如此的系統將能為駕駛節省停車時間，同時也提升了停車場管理效率。本引導系統包含兩個分支系統，分別是停車場系統以及無人機系統。停車場系統包含了空車位選擇以及路徑規劃，系統會根據當前剩餘空車位，透過A演算法(A algorithm)對每一個空車位做評估，進而找到距離停車場入口最近的空車位，並且規畫出無人機引導的最短路徑。無人機系統則包含了室內定位以及引導車輛狀態追蹤方法，由於室內收不到GPS訊號，因此本系統基於AprilTag標籤來協助無人機完成飛行引導任務。另外引導車輛狀態追蹤方法可分為四個功能，第一個功能為車輛偵測，採用Yolov4 tiny輕量化網路模型做車輛偵測，在足夠達到偵測效果的同時減少嵌入式系統的計算資源；第二個功能為汽車追蹤，透過改良之SORT演算法(Simple Online And Realtime Tracking)用以追蹤被引導車輛的動態；第三個功能為汽車特徵比對，當追蹤失敗系統會自動啟動汽車特徵比對的功能，透過ORB演算法(Oriented FAST and Rotated BRIEF)找尋追蹤目標特徵，接著使用FLANN匹配器(Fast Libary for Approximate Nearest Neighbors)做目標配對，最終將目標成功找回；第四個功能為等車機制，無人機在引導期間能夠隨時留意被引導汽車與無人機之間的距離，若離太遠會停止等待汽車跟上，落實引導目的。本論文最終於實際停車場環境驗證本系統的可行性，實測結果在無干擾環境下無人機確實能夠達成所需任務。;Car driving in the parking garage to find a parking space without any guidance mechanism often takes a lot of time. Maybe need to worry about taking a long detour to find a parking space, which is a long and laborious task, so this paper aims to establish an indoor parking system guided by a drone. First, the parking garage map needs to be built into the system. Then the system automatically screens the parking space closest to the parking garage entrance, plans the shortest path to this parking space, and then guides the car according to this path through the drone. After the guidance mission, the drone will take an image of the car that has completed the parking and send it back to the system to update the parking space status. Such a system can save drivers′ parking time and make parking lot management more effective. This guidance system consists of two subsystems: the parking garage and drone systems. The parking garage system includes parking space selection and path planning. The system will evaluate each vacant parking space through the A* algorithm to find the target parking space closest to the entrance and plan the shortest path for the drone guidance. The drone system includes indoor positioning and guided car status tracking method. Because the GPS signal cannot be received indoors, the system is based on the AprilTag to assist the drone in completing the flight guidance task. The guided car status tracking method can be divided into four functions. The first is car detection, and the Yolov4 tiny, lightweight network model is used to do car detection, which is sufficient to achieve the detection effect and reduce the computing resources of the embedded system. The second is car tracking, by improving the SORT algorithm(Simple Online And Realtime Tracking) to track guided car dynamics. The third is the search mechanism, and the system will automatically start the function of searching for the tracking target when the tracking fails, find the tracking target feature through the ORB algorithm(Oriented FAST and Rotated BRIEF), and then use the FLANN matcher(Fast Libary for Approximate Nearest Neighbors) to do target pairing, the target will be successfully retrieved finally. The fourth is the waiting mechanism, the drone can keep an eye on the distance between the guided car and the drone at any time during guidance, and if the distance is too far, the drone will stop and wait for the car to follow up to implement the guidance purpose. This paper verifies the system′s feasibility in the actual parking garage environment. The measured results show that the drone can achieve the required tasks in a non-interference environment.
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