本文選題：SLAM　切入點(diǎn)：擴(kuò)展卡爾曼　出處：《山東大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：機(jī)器人技術(shù)自問(wèn)世以來(lái),就得到了世界各國(guó)的重視,尤其是對(duì)智能移動(dòng)機(jī)器人SLAM的研究,一直以來(lái)都是機(jī)器人技術(shù)學(xué)科的研究熱點(diǎn),從1986年SLAM誕生至今已有三十年的發(fā)展,目前研究SLAM的基本框架基本已經(jīng)趨于穩(wěn)定,而且已經(jīng)有相當(dāng)多的應(yīng)用,但是研究的方法大都是基于高成本高精度的傳感器,導(dǎo)致不能夠?qū)崿F(xiàn)產(chǎn)業(yè)化,隨著近幾年的傳感器技術(shù)的發(fā)展,低成本的傳感器應(yīng)運(yùn)而生,為移動(dòng)機(jī)器人SLAM實(shí)現(xiàn)產(chǎn)業(yè)化提供了可能性,本文就是在這種情況下,研究與實(shí)現(xiàn)基于低成本的傳感器移動(dòng)機(jī)器人SLAM,主要研究的內(nèi)容如下:1)分析了 SLAM的分類(lèi)以及研究方法,并對(duì)比了各種研究SLAM的方法,同時(shí)根據(jù)SLAM的基本框架,構(gòu)建了基于低成本的傳感器的EKF-SLAM模型,使用卡爾曼理論解決移動(dòng)機(jī)器人位姿問(wèn)題,采用簡(jiǎn)單的碼盤(pán)和陀螺儀數(shù)據(jù)建立的數(shù)學(xué)模型,模型越簡(jiǎn)單系統(tǒng)誤差越小,系統(tǒng)的復(fù)雜性越低,越能接近實(shí)際,仿真與實(shí)際測(cè)試結(jié)果表明本文構(gòu)建的數(shù)學(xué)模型的有效性。2)針對(duì)SLAM中地圖構(gòu)建的問(wèn)題,本文采用低成本的激光傳感器實(shí)現(xiàn)ICP-SLAM,實(shí)現(xiàn)了經(jīng)典ICP算法以及PLICP,對(duì)比兩者之間實(shí)驗(yàn)結(jié)果,同時(shí)為了防止算法失效本文采用EKF輔助的方法,當(dāng)激光點(diǎn)匹配率低于60%的時(shí)候就代表該次匹配失效,為了防止缺失該時(shí)刻的地圖,采用EKF在此時(shí)刻的估計(jì)位姿作為ICP算法的旋轉(zhuǎn)R和平移T矩陣,由此機(jī)器人行走不至于很快的缺失環(huán)境信息,實(shí)驗(yàn)結(jié)果證明了提出的方法的有效性。3)為了提高精度,本文提出另一種方法,基于模糊理論信任度的二次數(shù)據(jù)融合,融合航跡推算位姿,EKF估計(jì)位姿以及ICP配準(zhǔn)位姿,使用一種模糊隸屬度函數(shù)描述對(duì)三者位姿的信任程度,通過(guò)計(jì)算三者的信任度矩陣,以及對(duì)應(yīng)的權(quán)值,最終做出決策對(duì)EKF和ICP的信任程度。
[Abstract]:Since the birth of robot technology, it has been paid attention to by many countries all over the world, especially the research on intelligent mobile robot (SLAM), which has been the research hotspot of robotics science all the time. It has been 30 years since SLAM was born in 1986. At present, the basic framework of SLAM has become stable, and has been widely used. However, most of the research methods are based on high-cost and high-precision sensors, which lead to the inability to realize industrialization. With the development of sensor technology in recent years, low-cost sensors emerge as the times require, which provides the possibility for the industrialization of mobile robot SLAM. This paper studies and implements the sensor mobile robot slam based on low cost. The main research contents are as follows: 1) the classification and research methods of SLAM are analyzed, and the methods of studying SLAM are compared. At the same time, according to the basic framework of SLAM, the classification and research methods of SLAM are compared. The EKF-SLAM model based on low cost sensor is constructed, and the position and pose problem of mobile robot is solved by Kalman theory. The simpler the model is, the smaller the system error is, and the more simple the model is, the smaller the system error is. The lower the complexity of the system, the closer it is to reality. The simulation and actual test results show that the mathematical model constructed in this paper is effective. 2) aiming at the problem of map construction in SLAM, In this paper, the ICP-SLAM is implemented with a low cost laser sensor, the classical ICP algorithm and the PLICP algorithm are implemented, and the experimental results between the two algorithms are compared. In order to prevent the algorithm from failing, this paper uses the EKF aided method. When the laser point matching rate is lower than 60%, it represents the failure of the matching. In order to avoid missing the map of the time, the estimated position and orientation of EKF at this time are used as the rotation R and the translation T matrix of the ICP algorithm. The experimental results show that the proposed method is effective. 3) in order to improve the accuracy, another method is proposed in this paper, which is based on the fuzzy theory trust degree of quadratic data fusion. Fusion track estimation EKF estimation and ICP registration pose, a fuzzy membership function is used to describe the degree of trust of the three postures. The trust matrix and the corresponding weights of the three positions are calculated. Finally make a decision about the degree of trust in EKF and ICP.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP242

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