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复杂环境下基于视觉信息的机器人定位研究
其他题名Localization For Mobile Robot Based on Visual Information in Large Scale Environment
赵增顺
学位类型工学博士
导师谭民 ; 侯增广
2007-06-05
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词机器人定位 图像分割 竞争学习 隐马尔可夫模型 位姿估计 Mobile Robot Localization Image Segmentation Competitive Learning Hidden Markov Model Pose Estimation
摘要定位是移动机器人研究中一项重要的内容,也是实现导航等其他功能的前提和保证。机器人视觉是智能机器人的一个重要分支,主要完成对外界环境的感知、描述、识别和理解,是产生行为决策的一个重要条件。但是机器人视觉存在容易受环境噪声干扰的缺点,从而降低了定位算法的鲁棒性。针对该问题,本文对如何基于单目视觉在复杂环境下解决“感知混淆”和不确定性问题,对复杂环境下基于视觉的移动机器人定位进行了研究。 鲁棒的图像特征对于机器人的视觉定位而言至关重要。针对SIFT特征计算量大、生成数量多、缺少彩色信息的三个缺点,本文对特征点邻域的HSI空间信息与SIFT特征进行融合,提出了鲁棒的HSI-SIFT图像局部特征。并且分别利用HSI信息、室内环境下平面运动以及主元分析的方法对HSI-SIFT图像局部特征进行了简化。HSI-SIFT特征在复杂背景(照明变化、存在遮挡、视角变化、环境布置有动态变化)可以实现鲁棒地实现图像特征匹配。 对训练学习阶段采集的多幅图像的HSI-SIFT图像局部特征进行双向降维,利用一种改进的RPCL竞争学习进行聚类分析。采用离线训练的方法,得到混杂压缩的HSI-SIFT原型特征库。 几何度量建模和拓扑图结构建模各有优缺点。本文针对大范围的复杂环境,集成了几何度量和拓扑方法的分级混杂建模方法,提出了一种生成全局拓扑、由粗到精多尺度分解、最底层拓扑节点基于局部几何度量模型的等级式混杂模型。 提出了一种基于主元变化彩色图像自适应分割门牌识别的方法,以此实现移动机器人在该拓扑地图中的定位。另外通过提取当前测试图像中HSI-SIFT图像局部特征,加载HSI-SIFT原型特征库,利用投票的策略进行机器人的拓扑定位。,采用基于隐马尔可夫模型和HSI-SIFT图像特征的定位方法,解决了感知混淆(perceptual aliasing)和图像变化(image variability)造成的拓扑定位不可靠问题。 针对单目视觉没有深度信息的缺点,借鉴双目立体视觉的原理,提出了基于单目视觉的移动机器人几何度量定位方法。
其他摘要Localization is an important part in the field of mobile robot, also it is precondition and guarantee for mobile robot’s navigation. In mobile robot’s localization, visual information are widely adopted. But visual information are potentially interfered by different noise exiting around the surroundings. So robustness of localization algorithm may be depressed because of these noises. To solve the “perception alias” problem and uncertainty in the large scale environment, this thesis proposed mobile robot’s localization based on monocular visual sensor . HSI-SIFT interest point feature is an robust algorithm for feature description under complex environment and variable illumination, which can achieve successful feature matching against scale change, partial occlusion, cluttered background, and scene dynamic. HSI-SIFT local visual features are extracted from multiple images obtained in the tranining stage. PCA is applied to reduce the quantity, size and complexity, and then, a modified robust RPCL algorithm is implemented to learn the natural cluster of these reduced features. The Location Prototype Feature Models of each topological nodes are constructed according to individual locations. As is well known, topological and metric mapping both have its own advantages and disadvantages. A hybrid hierarchical environment modeling is proposed which combines topological modeling and feature-based metric modeling method to extend to large scale environments, thus reducing the computation complexity and improving the precision of environment. The main idea is to connect local metric models by means of multi-scale hierarchical topological models. THSI allows a compact environment model which does not require global metric consistency and permits both precision and robustness. Global localization is the most important behaviors for autonomous navigation of robot, for which robustness and precision a necessity. Localization based on our proposed hybrid hierarchical environment modeling can achieve robust global localization and precise positioning. Door-plate numbers are selected as visual landmarks for topological localization. An adaptive colorful segmentation approach based on PCA and scale-space filtering is proposed. We can also use HSI-SIFT local feature in query image to recognize current locations via voting schemes. Furthermore, the probabilistic method based on a recursive Bayesian Filtering can be adopted to solve the problem of perceptual aliasing and image variability. A method which can obtain the metric pose estimation by monocular vision is proposed which borrows the concept of binocular vision.
馆藏号XWLW1092
其他标识符200418014628036
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5999
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
赵增顺. 复杂环境下基于视觉信息的机器人定位研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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