本文關(guān)鍵詞：基于四旋翼平臺(tái)的融合單目視覺與慣性傳感的里程計(jì)方法研究　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 四旋翼 國(guó)際空中機(jī)器人大賽 光流法 單目視覺里程計(jì) 擴(kuò)展卡爾曼濾波

【摘要】：本論文關(guān)注融合單目視覺和慣性傳感的里程計(jì)方法研究,旨在實(shí)現(xiàn)能在四旋翼飛行器平臺(tái)上實(shí)時(shí)運(yùn)算,并且具備高精度和魯棒性的自主定位算法,主要分為兩個(gè)場(chǎng)景。首先在筆者參加的國(guó)際空中機(jī)器人大賽(the International Aerial Robotics Competition,IARC)中要求飛行器在不依賴外界輔助導(dǎo)航的條件下實(shí)現(xiàn)自主定位。針對(duì)該比賽的特殊場(chǎng)景(地面具有豐富紋理信息和規(guī)則網(wǎng)格特征),提出了一種基于光流法和網(wǎng)格信息,同時(shí)融合慣性測(cè)量單元(Inertial measurement unit,IMU)的定位方法。.首先在傳統(tǒng)光流法上作了改進(jìn),基于固定塊匹配方法,實(shí)時(shí)獲取相機(jī)平動(dòng)速度,然后將其積分作為初始估計(jì),并通過地面網(wǎng)格信息來(lái)校正光流積分得到的位置信息,最后融合IMU數(shù)據(jù)進(jìn)行信號(hào)平滑,確保飛行器位姿無(wú)累積誤差。該方法在2016年IARC亞太賽區(qū)比賽中得到成功應(yīng)用。其次針對(duì)一般場(chǎng)景,選擇基于稀疏直接法的單目視覺里程計(jì)算法,無(wú)需計(jì)算每幀圖像的特征描述子,計(jì)算速率提高,并且設(shè)計(jì)了模塊化的擴(kuò)展卡爾曼濾波(extend Kalman Filter,EKF)框架,融合單目視覺里程計(jì)計(jì)算得到的相機(jī)位姿和IMU數(shù)據(jù)。對(duì)于預(yù)測(cè)部分,基于IMU驅(qū)動(dòng)系統(tǒng)的誤差狀態(tài)運(yùn)動(dòng)學(xué)實(shí)現(xiàn),對(duì)于測(cè)量部分,由視覺里程計(jì)提供的位置和姿態(tài)作為量測(cè),另外進(jìn)行了測(cè)量量和狀態(tài)量的時(shí)間同步處理,以及視覺算法位姿檢測(cè)失敗時(shí)的校正處理。在開源數(shù)據(jù)集測(cè)試本算法的準(zhǔn)確性和魯棒性。
[Abstract]:In this paper, we focus on the research of odometer based on monocular vision and inertial sensing, aiming to achieve real-time computing on the platform of four-rotors, and have high accuracy and robustness of autonomous positioning algorithm. It is divided into two main scenes. Firstly, I participated in the International Air Robot Competition (. The International Aerial Robotics Competition. IARC requires the aircraft to achieve autonomous positioning without relying on external navigation. The special scene of the game (ground has rich texture information and regular grid features). Based on optical flow method and grid information, an inertial measurement unit is proposed. Firstly, the traditional optical flow method is improved. Based on the fixed block matching method, the camera translational velocity is obtained in real time, and then the integral is used as the initial estimation. And through the ground grid information to correct the optical flow integration of the position information, finally fusion of IMU data for signal smoothing. This method has been successfully applied in the 2016 IARC Asia Pacific Competition. Secondly, for general scenarios, a single vision mileage calculation method based on sparse direct method is selected. The computation rate is improved without calculating the feature descriptor of each frame image, and a modular extended Kalman filter extend Kalman filter (EKF) framework is designed. For the prediction part, the error state kinematics of the IMU drive system is realized, and the measurement part. The position and attitude provided by the vision odometer are used as the measurements, and the time synchronization of the measurement and the state is also carried out. The accuracy and robustness of this algorithm are tested in open source data sets.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP242

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相關(guān)期刊論文 前1條

1 夏凌楠;張波;王營(yíng)冠;魏建明;;基于慣性傳感器和視覺里程計(jì)的機(jī)器人定位[J];儀器儀表學(xué)報(bào);2013年01期

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本文編號(hào)：1437575