基于视觉—激光的移动机器人自定位研究
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摘要
智能移动机器人研究属于多学科交叉领域,它的研究越来越受到国内外学者的重视。移动机器人自主定位是智能导航和环境探索研究的基础,是机器人实现真正智能化和完全自主的关键技术。作为一个复杂的系统性问题,机器人的自定位需要综合考虑传感器特性、作业环境特征和定位算法采用的具体形式等。本文系统地研究了智能移动机器人的自定位。首先概述了移动机器人自定位研究的国内外现状,阐述了定位研究的主要方法、关键问题以及技术发展趋势。从两个方面概述本文解决自定位的思想:一方面是将该问题总体建立在传感器数据融合基础上,通过先验地图并结合传感器模型提出相应的环境特征提取,然后给出相应的计算方法估计机器人的位姿状态:另一方面,本文又从模式分类角度解释机器人认识客观世界的过程。本文通过大量真实环境下的实验分析、比较了所提方法的性能。
面向RobCup中型移动机器人足球比赛以及SmartROB2嵌入式移动机器人平台,本文首先实现了基于单向视觉的移动机器人自定位任务。开发了基于MAP位姿估计的自定位系统,提出针对比赛环境的三维矢量表述形式,充分研究了单向视觉的成像特点并利用Unscented Transform有效传播系统内部的不确定性,实验表明基于MAP的迭代优化算法能够提高位姿估计的精度。
面向大型室内结构化环境的移动机器人视觉定位任务,要以有效的方式理解,解释以及表达工作环境的相关信息,才利于机器人对场景的有效分析。本文阐述了基于全向视觉的室内走廊环境的自定位,开发了基于Pioneer3D的人机交互式可视化自定位系统,提出几何—拓扑混合三维地图的环境地图,给出了全向传感器成像模型以及基于反馈分层估计融合的自定位算法。实验分析了不同初始位姿和观测信息下定位系统的精度和位姿估计的收敛情况,在考虑动态障碍物的遮挡情况下完成了机器人的在线环境感知和运动自定位任务。
考虑采用两种不同方式实现机器人在复杂室内环境中的自定位任务,一种是面向结构化走廊环境,借助单向摄象机实现基于地图的机器人自定位;另一方面,在处理办公室准结构化环境时,本文采用基于激光测距仪的扫描匹配方法,分析比较了ICP、Mb—ICP以及融合多次扫描匹配的自定位方法。
面向基于图像外观表象的移动机器人自定位任务,本文研究了描述机器人作业空间的特殊模型—基于外观的环境建模方法。分别研究了基于批处理PCA以及递增PCA的图像外观数据空间的构建方法,然后进一步给出了基于广义回归神经网络(GeneralizedRegression Neural Networks)的数据映射方法。
Autonomous mobile robot is the research focus in the field of robotics and automation. Self-localization is one of the foremost problems for intelligent navigation and envoriment exploration. As a complicated issue, self-loalization task should consider sensor characters, envorimental features and implementation of localization algorithms, et al. This dissertation provides a systematic research towards self-localization of mobile robot. We firstly expound state of the art in localization research, and present the leading methods, key technical issues and future development trends. To summarize the central contribution of this dissertation from two aspects, the first is to develop the localization methods based on multi-sensor fusion strategy; on the other hand, this dissertation interperate the cognitive procedure of robot from a statiscal pattern recognition viewpoint. Morever, this dissertation provides a mass of practical experiments based on real robot platform to verify the proposed methods.
Towards Robocup Middle-Sized Soccer, a localization system is developed for mobile robot. The robot estimates its pose recursively through a MAP estimator that incorporates the information collected from odometry and unidirectional camera. We build a 3D envorimental map for soccer field, the nonlinear sensor models and, maintain that the uncertainty manipulation of robot motion and inaccurate sensor measurements should be embedded and tracked throughout our system. We describe the uncertainty framework in a probabilistic geometry viewpoint and, use unscented transform to propagate the uncertainty which undergoes the given nonlinear functions. Considering the processing power of our robot, image features are extracted in the vicinity of corresponding projected features. In addition, data associations are evaluated by statistical distance. We conduct a series of systematic comparisons to prove the reliable and accurate performance of this self-localization system.
Towards large scale corridor environment, a novel metric-topological 3D map is proposed for robot self-localization based on omnidirectional vision. The local metric map, in a hierarchical manner, defines geometrical element according to its environmental feature level. Then, the topological parts in global map are used to connect the adjacent local maps. We design a nonlinear omnidirectional camera model to project the probabilistic map elements with uncertainty manipulation. For self-localization task, a human-machine interaction system is developed using hierarchical logic. It provides a fusion center which applies feedback hierarchical fusion method to fuse local estimates generated from multi-observations.
Without loss of generality, any indoor envoriment consists of at least two kinds of descriptions, namely, structural and semi-structural descriptions. The former is consistent with the proposed metric-topological 3D map, while the latter representing the office envoriment can not be simply modeled as a map. Therefore, we propose a hybrid localization system based on a sensor switching strategy between unidirectional camera and laser range finder. In this system, a sence analyzer is used to identify the envorimental features and, make desicisions on when to use camera or laser. In this way, the corresponding map-based and scan-matching methods are invoked when system is operating in camera and laser mode, respectively. Experimental results are shown accordingly.
Regression analysis between features of high-dimension is receiving attention in environmental learning of mobile robot. In this dissertation , we propose a novel framework, namely General regression neural network (GRNN), for approximating the functional relationship between high-dimensional map features and robot's states. We firstly adopt batch-PCA and increment-PCA to preprocess images taken from omnidirenctional vision. The method extracts map features optimally and reduces the correlated features while keeping the minimum reconstruction error. Then, the robot states and corresponding features of the training panoramic snapshots are used to train the given neural network.
面向RobCup中型移动机器人足球比赛以及SmartROB2嵌入式移动机器人平台,本文首先实现了基于单向视觉的移动机器人自定位任务。开发了基于MAP位姿估计的自定位系统,提出针对比赛环境的三维矢量表述形式,充分研究了单向视觉的成像特点并利用Unscented Transform有效传播系统内部的不确定性,实验表明基于MAP的迭代优化算法能够提高位姿估计的精度。
面向大型室内结构化环境的移动机器人视觉定位任务,要以有效的方式理解,解释以及表达工作环境的相关信息,才利于机器人对场景的有效分析。本文阐述了基于全向视觉的室内走廊环境的自定位,开发了基于Pioneer3D的人机交互式可视化自定位系统,提出几何—拓扑混合三维地图的环境地图,给出了全向传感器成像模型以及基于反馈分层估计融合的自定位算法。实验分析了不同初始位姿和观测信息下定位系统的精度和位姿估计的收敛情况,在考虑动态障碍物的遮挡情况下完成了机器人的在线环境感知和运动自定位任务。
考虑采用两种不同方式实现机器人在复杂室内环境中的自定位任务,一种是面向结构化走廊环境,借助单向摄象机实现基于地图的机器人自定位;另一方面,在处理办公室准结构化环境时,本文采用基于激光测距仪的扫描匹配方法,分析比较了ICP、Mb—ICP以及融合多次扫描匹配的自定位方法。
面向基于图像外观表象的移动机器人自定位任务,本文研究了描述机器人作业空间的特殊模型—基于外观的环境建模方法。分别研究了基于批处理PCA以及递增PCA的图像外观数据空间的构建方法,然后进一步给出了基于广义回归神经网络(GeneralizedRegression Neural Networks)的数据映射方法。
Autonomous mobile robot is the research focus in the field of robotics and automation. Self-localization is one of the foremost problems for intelligent navigation and envoriment exploration. As a complicated issue, self-loalization task should consider sensor characters, envorimental features and implementation of localization algorithms, et al. This dissertation provides a systematic research towards self-localization of mobile robot. We firstly expound state of the art in localization research, and present the leading methods, key technical issues and future development trends. To summarize the central contribution of this dissertation from two aspects, the first is to develop the localization methods based on multi-sensor fusion strategy; on the other hand, this dissertation interperate the cognitive procedure of robot from a statiscal pattern recognition viewpoint. Morever, this dissertation provides a mass of practical experiments based on real robot platform to verify the proposed methods.
Towards Robocup Middle-Sized Soccer, a localization system is developed for mobile robot. The robot estimates its pose recursively through a MAP estimator that incorporates the information collected from odometry and unidirectional camera. We build a 3D envorimental map for soccer field, the nonlinear sensor models and, maintain that the uncertainty manipulation of robot motion and inaccurate sensor measurements should be embedded and tracked throughout our system. We describe the uncertainty framework in a probabilistic geometry viewpoint and, use unscented transform to propagate the uncertainty which undergoes the given nonlinear functions. Considering the processing power of our robot, image features are extracted in the vicinity of corresponding projected features. In addition, data associations are evaluated by statistical distance. We conduct a series of systematic comparisons to prove the reliable and accurate performance of this self-localization system.
Towards large scale corridor environment, a novel metric-topological 3D map is proposed for robot self-localization based on omnidirectional vision. The local metric map, in a hierarchical manner, defines geometrical element according to its environmental feature level. Then, the topological parts in global map are used to connect the adjacent local maps. We design a nonlinear omnidirectional camera model to project the probabilistic map elements with uncertainty manipulation. For self-localization task, a human-machine interaction system is developed using hierarchical logic. It provides a fusion center which applies feedback hierarchical fusion method to fuse local estimates generated from multi-observations.
Without loss of generality, any indoor envoriment consists of at least two kinds of descriptions, namely, structural and semi-structural descriptions. The former is consistent with the proposed metric-topological 3D map, while the latter representing the office envoriment can not be simply modeled as a map. Therefore, we propose a hybrid localization system based on a sensor switching strategy between unidirectional camera and laser range finder. In this system, a sence analyzer is used to identify the envorimental features and, make desicisions on when to use camera or laser. In this way, the corresponding map-based and scan-matching methods are invoked when system is operating in camera and laser mode, respectively. Experimental results are shown accordingly.
Regression analysis between features of high-dimension is receiving attention in environmental learning of mobile robot. In this dissertation , we propose a novel framework, namely General regression neural network (GRNN), for approximating the functional relationship between high-dimensional map features and robot's states. We firstly adopt batch-PCA and increment-PCA to preprocess images taken from omnidirenctional vision. The method extracts map features optimally and reduces the correlated features while keeping the minimum reconstruction error. Then, the robot states and corresponding features of the training panoramic snapshots are used to train the given neural network.
引文
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