基于低成本激光傳感器移動機器人SLAM研究與實現
發(fā)布時間:2018-03-16 19:41
本文選題:SLAM 切入點:擴展卡爾曼 出處:《山東大學》2017年碩士論文 論文類型:學位論文
【摘要】:機器人技術自問世以來,就得到了世界各國的重視,尤其是對智能移動機器人SLAM的研究,一直以來都是機器人技術學科的研究熱點,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趨于穩(wěn)定,而且已經有相當多的應用,但是研究的方法大都是基于高成本高精度的傳感器,導致不能夠實現產業(yè)化,隨著近幾年的傳感器技術的發(fā)展,低成本的傳感器應運而生,為移動機器人SLAM實現產業(yè)化提供了可能性,本文就是在這種情況下,研究與實現基于低成本的傳感器移動機器人SLAM,主要研究的內容如下:1)分析了 SLAM的分類以及研究方法,并對比了各種研究SLAM的方法,同時根據SLAM的基本框架,構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機器人位姿問題,采用簡單的碼盤和陀螺儀數據建立的數學模型,模型越簡單系統(tǒng)誤差越小,系統(tǒng)的復雜性越低,越能接近實際,仿真與實際測試結果表明本文構建的數學模型的有效性。2)針對SLAM中地圖構建的問題,本文采用低成本的激光傳感器實現ICP-SLAM,實現了經典ICP算法以及PLICP,對比兩者之間實驗結果,同時為了防止算法失效本文采用EKF輔助的方法,當激光點匹配率低于60%的時候就代表該次匹配失效,為了防止缺失該時刻的地圖,采用EKF在此時刻的估計位姿作為ICP算法的旋轉R和平移T矩陣,由此機器人行走不至于很快的缺失環(huán)境信息,實驗結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據融合,融合航跡推算位姿,EKF估計位姿以及ICP配準位姿,使用一種模糊隸屬度函數描述對三者位姿的信任程度,通過計算三者的信任度矩陣,以及對應的權值,最終做出決策對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.
【學位授予單位】:山東大學
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:TP242
【參考文獻】
相關期刊論文 前7條
1 顧文華;周波;戴先中;;基于ICP匹配算法的室內移動機器人定位[J];華中科技大學學報(自然科學版);2013年S1期
2 高云峰;周倫;呂明睿;劉文濤;;自主移動機器人室內定位方法研究綜述[J];傳感器與微系統(tǒng);2013年12期
3 徐則中;莊燕濱;;移動機器人定位方法對比研究[J];系統(tǒng)仿真學報;2009年07期
4 胡勁草;;室內自主式移動機器人定位方法研究[J];機電產品開發(fā)與創(chuàng)新;2006年05期
5 李群明,熊蓉,褚健;室內自主移動機器人定位方法研究綜述[J];機器人;2003年06期
6 龔元明,蕭德云,王俊杰;多傳感器數據融合技術在自控垂鉆檢測系統(tǒng)中的應用[J];地球科學;2001年05期
7 徐國華,譚民;移動機器人的發(fā)展現狀及其趨勢[J];機器人技術與應用;2001年03期
相關碩士學位論文 前1條
1 孫玉梁;移動機器人室內即時地圖構建與自主導航[D];大連理工大學;2011年
,本文編號:1621414
本文鏈接:http://www.wukwdryxk.cn/shoufeilunwen/xixikjs/1621414.html
最近更新
教材專著