基于雷達和視覺復(fù)合傳感器的無人機障礙物檢測研究
發(fā)布時間：2019-06-14 09:12
【摘要】：具有自主飛行能力的無人機由于其成本低,機動性能好,效費比好,生存能力強,無人員傷亡風(fēng)險等優(yōu)點,在軍事、民用以及科學(xué)研究中均具有重要的應(yīng)用價值。然而現(xiàn)有的無人機缺乏自主探測及躲避障礙物的能力,在可視范圍以內(nèi)可以通過人為的遙控飛行,可視范圍以外障礙物會對無人機飛行產(chǎn)生安全威脅。因此需要構(gòu)建一套具有感知能力,并使無人機能夠?qū)崿F(xiàn)障礙物自主檢測的方法。本文提出了基于毫米波雷達和視覺傳感器融合進行無人機障礙物的檢測的方法。利用毫米波雷達獲取前方障礙物的距離、角度等信息,根據(jù)毫米波雷達獲取的信息和圖像的底層顏色信息在視頻圖像上建立起障礙物的感興趣區(qū)域,然后利用SURF(SpeededUp Robust Feature)算法對感興趣區(qū)域進行驗證,判斷是否為建筑物等障礙物。主要研究內(nèi)容如下:1.建立雷達坐標和圖像坐標轉(zhuǎn)換關(guān)系。根據(jù)無人機平臺高度、姿態(tài)變化的特點,雷達和圖像坐標轉(zhuǎn)換關(guān)系需要滿足對其變化具有自適應(yīng)性。傳統(tǒng)的毫米波雷達和視覺傳感器融合通常基于二維運動平臺的假設(shè),對平臺的姿態(tài)變化非常敏感。本文基于相機的針孔模型推導(dǎo)并建立與融合系統(tǒng)的姿態(tài)和高度關(guān)聯(lián)的雷達坐標和圖像坐標轉(zhuǎn)換關(guān)系,并通過融合IMU的姿態(tài)數(shù)據(jù)和差分GPS的高度數(shù)據(jù)精確計算出目標障礙物的圖像坐標。2.障礙物候選區(qū)域分割。由于毫米波雷達獲取的為環(huán)境的稀疏信息,不能得到障礙物的全面位置信息。因此根據(jù)雷達反射點的圖像坐標和圖像顏色特征進行障礙物的候選區(qū)域分割。3.基于SURF算法的障礙物候選區(qū)域判別。通過提取候選區(qū)域的SURF特征關(guān)鍵點與障礙物樣本的關(guān)鍵點數(shù)據(jù)庫匹配來實現(xiàn)障礙物的分類識別。4.融合系統(tǒng)開發(fā)及實驗驗證。利用VS2008和Opencv 2.43開發(fā)了基于毫米波雷達、視覺傳感器、IMU(Inertial Measurement Unit)和 GPS(Global Position System)多傳感器融合的無人機障礙物檢測系統(tǒng)。系統(tǒng)運行環(huán)境為研華Celeron 2.3GHz處理器、2G內(nèi)存的單板計算機,多傳感器融合障礙物檢測系統(tǒng)搭在無人機上進行低空障礙物檢測實驗。實驗結(jié)果表明,該方法可以自適應(yīng)無人機的姿態(tài)變化以及能夠快速、準確地實現(xiàn)無人機障礙物的在線檢測,具有較好的實時性和準確性。
[Abstract]:UAV with autonomous flight ability has important application value in military, civil and scientific research because of its low cost, good maneuverability, good efficiency-cost ratio, strong survivability, no risk of casualties and so on. However, the existing UAV lacks the ability to detect and avoid obstacles independently, and can fly by man-made remote control within the visible range, and obstacles outside the visible range will pose a safety threat to UAV flight. Therefore, it is necessary to construct a set of perceptual methods, which can enable UAV to detect obstacles autonomously. In this paper, a method of UAV obstacle detection based on millimeter wave radar and vision sensor fusion is proposed. The millimeter wave radar is used to obtain the distance and angle of the obstacle in front of the video image, and according to the information obtained by the millimeter wave radar and the color information of the bottom of the image, the region of interest of the obstacle is established on the video image, and then the region of interest is verified by SURF (SpeededUp Robust Feature) algorithm to determine whether it is an obstacle such as a building. The main research contents are as follows: 1. The transformation relationship between radar coordinates and image coordinates is established. According to the characteristics of UAV platform height and attitude change, the coordinate conversion relationship between radar and image needs to be adaptive to the change of UAV platform. The fusion of traditional millimeter wave radar and visual sensor is usually based on the assumption of two-dimensional motion platform and is very sensitive to the attitude change of the platform. In this paper, based on the pinhole model of the camera, the transformation relationship between radar coordinates and image coordinates related to the attitude and height of the fusion system is derived and established, and the image coordinates of the target obstacle are calculated accurately by combining the attitude data of IMU and the height data of differential GPS. 2. Obstacle candidate region segmentation. Due to the sparse information obtained by millimeter wave radar for the environment, the comprehensive position information of obstacles can not be obtained. Therefore, according to the image coordinates and image color characteristics of radar reflection points, the candidate regions of obstacles are segmented. Obstacle candidate region discrimination based on SURF algorithm. The classification and recognition of obstacles is realized by extracting the SURF feature key points of the candidate region and matching the key points of the obstacle samples. 4. Development and experimental verification of fusion system. An obstacle detection system for UAV based on millimeter wave radar, vision sensor, IMU (Inertial Measurement Unit) and GPS (Global Position System) is developed by using VS2008 and Opencv 2.43. The running environment of the system is Yanhua Celeron 2.3GHz processor, 2G memory single board computer, multi-sensor fusion obstacle detection system is built on UAV to carry out low altitude obstacle detection experiment. The experimental results show that the method can adapt to the attitude change of UAV and realize the on-line detection of UAV obstacles quickly and accurately, and has good real-time and accuracy.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：V279;TP391.41
本文編號：2499283
[Abstract]:UAV with autonomous flight ability has important application value in military, civil and scientific research because of its low cost, good maneuverability, good efficiency-cost ratio, strong survivability, no risk of casualties and so on. However, the existing UAV lacks the ability to detect and avoid obstacles independently, and can fly by man-made remote control within the visible range, and obstacles outside the visible range will pose a safety threat to UAV flight. Therefore, it is necessary to construct a set of perceptual methods, which can enable UAV to detect obstacles autonomously. In this paper, a method of UAV obstacle detection based on millimeter wave radar and vision sensor fusion is proposed. The millimeter wave radar is used to obtain the distance and angle of the obstacle in front of the video image, and according to the information obtained by the millimeter wave radar and the color information of the bottom of the image, the region of interest of the obstacle is established on the video image, and then the region of interest is verified by SURF (SpeededUp Robust Feature) algorithm to determine whether it is an obstacle such as a building. The main research contents are as follows: 1. The transformation relationship between radar coordinates and image coordinates is established. According to the characteristics of UAV platform height and attitude change, the coordinate conversion relationship between radar and image needs to be adaptive to the change of UAV platform. The fusion of traditional millimeter wave radar and visual sensor is usually based on the assumption of two-dimensional motion platform and is very sensitive to the attitude change of the platform. In this paper, based on the pinhole model of the camera, the transformation relationship between radar coordinates and image coordinates related to the attitude and height of the fusion system is derived and established, and the image coordinates of the target obstacle are calculated accurately by combining the attitude data of IMU and the height data of differential GPS. 2. Obstacle candidate region segmentation. Due to the sparse information obtained by millimeter wave radar for the environment, the comprehensive position information of obstacles can not be obtained. Therefore, according to the image coordinates and image color characteristics of radar reflection points, the candidate regions of obstacles are segmented. Obstacle candidate region discrimination based on SURF algorithm. The classification and recognition of obstacles is realized by extracting the SURF feature key points of the candidate region and matching the key points of the obstacle samples. 4. Development and experimental verification of fusion system. An obstacle detection system for UAV based on millimeter wave radar, vision sensor, IMU (Inertial Measurement Unit) and GPS (Global Position System) is developed by using VS2008 and Opencv 2.43. The running environment of the system is Yanhua Celeron 2.3GHz processor, 2G memory single board computer, multi-sensor fusion obstacle detection system is built on UAV to carry out low altitude obstacle detection experiment. The experimental results show that the method can adapt to the attitude change of UAV and realize the on-line detection of UAV obstacles quickly and accurately, and has good real-time and accuracy.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：V279;TP391.41
【參考文獻】
相關(guān)期刊論文 前10條
1 王蛟龍;周潔;高慧;秦娜;馬磊;;基于局部環(huán)境形狀特征識別的移動機器人避障方法[J];信息與控制;2015年01期
2 崔軍輝;魏瑞軒;張小倩;;無人機感知-規(guī)避系統(tǒng)安全區(qū)域動態(tài)決策方法[J];控制與決策;2014年12期
3 趙海;陳星池;王家亮;曾若凡;;基于四軸飛行器的單目視覺避障算法[J];光學(xué)精密工程;2014年08期
4 王海波;林久輝;李永濤;;軍用無人機的應(yīng)用與發(fā)展趨勢[J];科技視界;2014年15期
5 王飛;崔金強;陳本美;李崇興;;一套完整的基于視覺光流和激光掃描測距的室內(nèi)無人機導(dǎo)航系統(tǒng)(英文)[J];自動化學(xué)報;2013年11期
6 王東署;王佳;;未知環(huán)境中移動機器人環(huán)境感知技術(shù)研究綜述[J];機床與液壓;2013年15期
7 陳建;孫曉穎;林琳;王波;;一種高精度超聲波到達時刻的檢測方法[J];儀器儀表學(xué)報;2012年11期
8 雷艷敏;朱齊丹;仲訓(xùn)昱;關(guān)秀麗;;基于激光測距儀的障礙物檢測的仿真研究[J];計算機工程與設(shè)計;2012年02期
9 金林敏;鄭榮金;祁一民;;無人機在現(xiàn)代戰(zhàn)爭中的運用及發(fā)展[J];飛航導(dǎo)彈;2011年09期
10 江更祥;;淺談無人機[J];制造業(yè)自動化;2011年15期
相關(guān)碩士學(xué)位論文 前1條
1 崔燕茹;基于雙目視覺的障礙物識別與重建[D];南昌航空大學(xué);2012年
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