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研究了室内自主移动机器人的即时定位与地图创建问题。分析了目前解决SLAM问题的方法,提出了基于扫描匹配预处理的即时定位与地图创建,用扫描匹配为SLAM提供机器人先验位姿信息。对实验结果和数据的分析,得出了所提出方法可进一步提高SLAM的精度和鲁棒性。 相似文献
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未知环境中移动机器人SLAM问题的研究进展 总被引:1,自引:14,他引:1
移动机器人的定位与地图创建是机器人研究中一个基础且重要的问题。本文对该领域的最新进展进行了综述.特别侧重于未知环境中机器人并发定位与地图创建(SLAM)问题;比较详细地分析了地图表示方法、定位和环境特征的提取、不确定信息的表示和处理等关键技术:同时对几种典型的SLAM方法进行了介绍:阐述了移动机器人SLAM问题研究中所面临的主要问题.并探计了将来的发展方向。 相似文献
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移动机器人同时定位和地图创建是实现移动机器人完全自主导航的关键.本文提出了一个通用的移动机器人同时定位与地图创建基本框架,接着对扩展卡尔曼滤波器算法进行了详细的分析,最后通过基于点特征和扩展卡尔曼滤波器的同时定位与地图创建仿真实验,验证了框架的可行性.目的是为开展同时定位与地图创建的研究提供一种可行的研究方案,以推动我国移动机器人技术的发展. 相似文献
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为了更有效、可靠地从传感器原始数据中获取信息,介绍了一种移动机器人同步定位与地图创建的方法。该方法使用二维激光测距传感器实现室内环境中的移动机器人自主定位,依靠无嗅卡尔曼滤波器减少定位过程中所产生的误差;通过激光测距仪采集机器人所在环境数据的曲率函数,将环境特征分解为直线、拐角和曲线三类基本定位特征,并结合环境地图得到机器人位置和姿态的最优解。试验结果表明,该定位方法对于室内环境是有效的。 相似文献
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一种基于特征地图的移动机器人SLAM方案 总被引:1,自引:0,他引:1
设计了一种结构化环境中基于特征地图的地图创建方案;采用激光测距仪进行特征地图创建,利用"聚合-分害虫-聚合"的方法来提取线段表示环境信息实现局部地图创建;为了实现移动机器人的同时定位与地图创建,采用扩展卡尔曼滤波方法对机器人的位姿与地图信息进行预测及更新,结合状态估计和数据关联理论,实验显示x的校正量保持在±0.9cm之内;y的校正量保持在±2.5cm之内;θ的校正量在±1.2之内,实现了基于扩展卡尔曼滤波器的SLAM. 相似文献
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移动机器人的同步自定位与地图创建研究进展 总被引:20,自引:2,他引:18
自主移动机器人在未知环境下作业时,首先要解决的基本问题就是其自身的定位问题,而定位问题与环境地图的创建又是相辅相成的.本文从相关理论和关键技术等方面,系统地总结了同步自定位和地图创建的研究现状,着重介绍了基于概率论的方法,分析了目前存在的难题,并指出了未来研究的发展方向. 相似文献
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《电子制作.电脑维护与应用》2016,(8)
移动机器人是目前的研究热点,而同时定位与地图创建是机器人研究领域难点,是实现机器人在未知环境下自主导航的前提。目前,对于机器人在已知地图的自主定位已经有了多种解决办法,但是随着工业的发展和人类探索领域的扩大,在一些对人有危害性的作业现场(如有毒气体检测、矿山开采,灾难抢险等)或者不适合人类工作的地域(如海洋和海底探测、星际探险等),人们希望机器人能够代替人类工作,实现难以获得机器人工作的环境信息和定位信息。而移动机器人的同时定位与地图创建SLAM则能够较好的解决这个问题。 相似文献
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《Advanced Robotics》2013,27(8-9):1055-1074
Abstract Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM. 相似文献
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An autonomous mobile robot must have the ability to navigate in an unknown environment. The simultaneous localization and
map building (SLAM) problem have relation to this autonomous ability. Vision sensors are attractive equipment for an autonomous
mobile robot because they are information-rich and rarely have restrictions on various applications. However, many vision
based SLAM methods using a general pin-hole camera suffer from variation in illumination and occlusion, because they mostly
extract corner points for the feature map. Moreover, due to the narrow field of view of the pin-hole camera, they are not
adequate for a high speed camera motion. To solve these problems, this paper presents a new SLAM method which uses vertical
lines extracted from an omni-directional camera image and horizontal lines from the range sensor data. Due to the large field
of view of the omni-directional camera, features remain in the image for enough time to estimate the pose of the robot and
the features more accurately. Furthermore, since the proposed SLAM does not use corner points but the lines as the features,
it reduces the effect of illumination and partial occlusion. Moreover, we use not only the lines at corners of wall but also
many other vertical lines at doors, columns and the information panels on the wall which cannot be extracted by a range sensor.
Finally, since we use the horizontal lines to estimate the positions of the vertical line features, we do not require any
camera calibration. Experimental work based on MORIS, our mobile robot test bed, moving at a human’s pace in the real indoor
environment verifies the efficacy of this approach. 相似文献
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《Computer Vision and Image Understanding》2010,114(2):286-296
We propose to use a multi-camera rig for simultaneous localization and mapping (SLAM), providing flexibility in sensor placement on mobile robot platforms while exploiting the stronger localization constraints provided by omni-directional sensors. In this context, we present a novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion. Our approach circumvents the combinatorial data association problem by using an incremental expectation maximization algorithm. In the expectation step we determine a distribution over correspondences by sampling. In the maximization step, we find optimal parameters of a density over the robot motion and environment structure. By summarizing the sampling results in so-called virtual measurements, the resulting optimization simplifies to the equivalent optimization problem for known correspondences. We present results for simulated data, as well as for data obtained by a mobile robot equipped with a multi-camera rig. 相似文献
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提出了一种新颖的无线传感器网络(WSN)辅助的移动机器人同步定位与地图创建(SLAM)方法,
解决了传统SLAM 方法难以解决的求解问题空间维数高和多数据关联困难两大问题.为该WSN 辅助的SLAM
方法建立了模型,并进行了噪声分析;在此基础上,提出一种适用本方法的分布式粒子滤波数据融合算法.着重
分析了粒子初始化、预测、序贯重要性采样和重采样等关键步骤,并通过仿真实验分析验证了该方法的正确性和
高效率.实验结果表明,采用粒子滤波算法,并综合无线传感器网络进行辅助导航,可以极大地降低求解问题空
间维数,解决多数据关联错误问题,可以完全不依赖锚节点完成盲节点高精度定位;同时,还能够有效地提高移
动机器人定位与地图创建精度,特别是在不要求机器人路径闭合的情况下可以有效抑制惯性导航的误差累计. 相似文献
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基于视觉的同时定位与地图构建方法综述 总被引:4,自引:1,他引:3
基于视觉的自主导航与路径规划是移动机器人研究的关键技术,对基于视觉的计算机导航与同时定位及地图构建(SLAM)方法近三十年的发展进行了总结和展望。将视觉导航分为室内导航和室外导航,并详细阐述了每一种子类型的特点和方法。对于室内视觉导航,列举了经典导航模型和技术方法,探讨了解决SLAM问题的最新进展:HTM-SLAM算法和基于特征的算法;对室外视觉导航,阐述了国际国内目前的研究动态。 相似文献
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《Advanced Robotics》2013,27(12-13):1601-1616
This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments. 相似文献
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Simultaneous localization and mapping (SLAM) in unknown GPS‐denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single‐robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state‐of‐the‐art multiple‐robot systems, with a major focus on multiple‐robot SLAM. Various issues and problems in multiple‐robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed. 相似文献