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提出一种改进的粒子滤波SLAM(simultaneous localization and map building)同时定位和地图创建实现方法。改进方法让机器人大约行进10步完成基于局部已创建地图下的粒子滤波定位后,再利用激光传感器探测环境并更新创建的地图;同时在利用粒子滤波定位时,使粒子只分布在由航位推算法得出的机器人位姿附近,从而可有效地减少粒子的数量。实验结果表明,与标准的粒子滤波SLAM 算法比较,改进算法提高了机器人SLAM过程中定位和地图创建的精度和实时性,并为移动机器人在室外未知环境同时定位和地图创建提供了新方法。 相似文献
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机器人同步定位与地图构建(SLAM)是指机器人在移动过程中以增量形式创建环境地图并通过所构建地图反复推断自身位置的过程.为实现上述功能,采用传统的扩展卡尔曼滤波(EKF)最优迭代估计方法,在大范围环境条件下,估计误差累积增大,且不能对已构建的环境地图进行更新.提出一种改进算法(KLM-EKF算法),用已知路标的信息对机器人位姿和协方差矩阵进行修正,并创建辅助系数矩阵修正已构建地图,从而实现路标的全局更新.仿真结果表明,在大范围环境中,改进后的算法使机器人自身定位和路标估计误差大幅度降低,并且能够自主地更新已构建地图,有效提高了定位和构图精度. 相似文献
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针对未知环境中移动机器人同时定位和地图创建(Simultaneous Localization and Map Building,SLAM)由于机器人位姿和环境地图都不确定导致定位和地图创建变得更加复杂,提出一种局部最优(全局次优)参数法,即通过局部最优的位姿创建局部最优的环境地图,再通过局部最优的环境地图寻求局部最优的位姿,如此交替进行,直到得到全局确定性的位姿和确定性的环境地图。实验结果表明,同标准的基于粒子滤波的SLAM 算法(Particle Filtering-SLAM,PF-SLAM)比较,改进的算法提高了机器人SLAM过程中定位的准确度和地图创建的精确度,为机器人在未知的室外大环境同时定位和地图创建提供新的方法。 相似文献
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针对煤矿井下无GPS环境下巡检机器人自主定位问题,研究了基于激光雷达的同步定位与地图构建方法。首先建立激光雷达观测模型和里程计预测模型,将机器人定位和地图构建的实际问题转换为概率数学模型的逻辑推理问题。同时采用自适应蒙特卡罗定位算法进行机器人实时位姿估计,提出了根据粒子权重(地图的匹配度)进行重采样的方法,以去除权重小的粒子,实现了用较少、较好粒子精确表达机器人位姿的后验概率分布,满足机器人利用传感器在栅格地图上实时定位的需求。通过对Fast-SLAM算法进行优化,减少了粒子数量,缓解了粒子耗散,提高了地图构建的精确性。实验结果表明,基于激光雷达的同步定位与地图构建方法有效解决了巡检机器人实时位姿估计和环境地图构建的问题,结合自适应蒙特卡罗定位算法和优化Fast-SLAM算法提高了机器人定位的自适应性和地图构建的精确性。 相似文献
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解决同时定位与地图构建(SLAM)问题是实现机器人自主导航的核心.目前,Rao-Blackwellized粒子滤波器(RBPF)是解决机器人同时定位与地图构建的有效方法.该方法在计算提议分布时,通常只考虑移动机器人的里程计信息,因此存在需要大量的采样粒子造成的计算量和复杂度增大的问题.本文提出一种改进算法,在计算提议分布时将机器人里程计信息和激光传感器采集的距离信息进行融合,有效地减少了所需粒子的数量并降低了滤波器预测阶段机器人位姿的不确定性.本文在机器人操作系统(robot operating system,ROS)平台上,使用配有URG激光器的Pioneer3-DX机器人进行了实验.结果表明,采用本文方法能够实时在线地创建高精度的栅格地图,为机器人在未知环境中的SLAM和导航提供了新途径. 相似文献
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移动机器人即时定位与地图创建问题研究 总被引:26,自引:2,他引:26
基于环境特征的移动机器人即时定位与地图创建是机器人领域的开放性课题.本文从环境特征提取、定位与地图创建、数据相关三个方面对移动机器人即时定位与地图创建问题进行了综述.对移动机器人定位问题作了概述.探讨了室内环境下特征提取方法.详细地论述了定位与地图创建中面临的主要问题及其解决方法;阐述了数据相关问题的基本思想.最后,根据近期文献指出了该领域今后的研究方向.
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多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping, CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同, 对多机器人CSLAM算法研究现状和进展进行综述.首先,简要介绍了基于完全连通通信条件的集中式CSLAM算法的特点和缺陷; 其次,结合多机器人系统初始相对位姿关系未知的情况,从地图配准、数据关联和地图融合等三个方面, 对基于通信范围或者带宽受限条件的分布式CSLAM算法的地图合并问题进行了分析和阐述; 进而重点对考虑稀疏动态通信状况的分布式CSLAM算法的最新研究成果进行了归纳总结. 最后指出多机器人CSLAM研究领域今后的研究方向. 相似文献
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Vision-based global localization and mapping for mobile robots 总被引:14,自引:0,他引:14
We have previously developed a mobile robot system which uses scale-invariant visual landmarks to localize and simultaneously build three-dimensional (3-D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3-D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scale-invariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map. 相似文献
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Robot semantic mapping through human activity recognition: A wearable sensing and computing approach
Semantic information can help robots understand unknown environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a new robot semantic mapping approach through human activity recognition in a human–robot coexisting environment. An intelligent mobile robot platform called ASCCbot creates a metric map while wearable motion sensors attached to the human body are used to recognize human activities. Combining pre-learned models of activity–furniture correlation and location–furniture correlation, the robot determines the probability distribution of the furniture types through a Bayesian framework and labels them on the metric map. Computer simulations and real experiments demonstrate that the proposed approach is able to create a semantic map of an indoor environment effectively. 相似文献
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Appearance-based mapping and localization for mobile robots using a feature stability histogram 总被引:1,自引:0,他引:1
The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information. However, developing a mapping and a localization method using these kinds of models is very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. In other words, the mobile robot has to deal with environmental appearance change, which modifies its representation of the environment. This paper proposes an indoor appearance-based mapping and a localization method for mobile robots based on the human memory model, which was used to build a Feature Stability Histogram (FSH) at each node in the robot topological map. This FSH registers local feature stability over time through a voting scheme, and the most stable features were considered for mapping, for Bayesian localization and for incrementally updating the current appearance reference view in the topological map. The experimental results are presented using an omnidirectional images dataset acquired over the long-term and considering: illumination changes (time of day, different seasons), occlusions, random removal of features, and perceptual aliasing. The results include a comparison with the approach proposed by Dayoub and Duckett (2008) [19] and the popular Bag-of-Words (Bazeille and Filliat, 2010) [35] approach. The obtained results confirm the viability of our method and indicate that it can adapt the internal map representation over time to localize the robot both globally and locally. 相似文献
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在不使用几何参数描述大规模环境的前提下, 提出了基于分治法的同步定位与环境采样地图创建 (Simultaneous localization and sampled environment mapping, SLASEM)算法来同时进行定位与地图创建. 该算法采用环境采样地图(Sampled environment map, SEM)描述环境, 使算法不局限于用几何参数描述的规则环境. 同时该算法实时地创建局部地图, 并基于分治法合并局部地图, 保证了算法的实时性. 在合并两个子地图时, 算法首先从环境采样地图中提取出角点, 利用角点约束初步更新子地图; 然后利用符号正交距离函数作为虚拟测量函数, 再次细微地更新子地图; 最后将两个子地图合并到一个大地图, 约简冗余的环境采样粒子, 以提高地图的紧凑性. 两个实验的结果验证了所提算法的有效性和实时性. 相似文献
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移动机器人基于激光测距和单目视觉的室内同时定位和地图构建 总被引:16,自引:1,他引:16
该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性. 相似文献
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To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot. 相似文献
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《Advanced Robotics》2013,27(6-7):941-962
In this paper we present an algorithm for the application of simultaneous localization and mapping in complex environments. Instead of building a grid map or building a feature map with a small number of the obstacles' geometric parameters, the proposed algorithm builds a sampled environment map (SEM) to represent a complex environment with a set of environment samples. To overcome the lack of one-toone correspondence between environment samples and raw observations, the signed orthogonal distance function is proposed to be used as the observation model. A method considering geometric constraints is presented to remove redundant environment samples from the SEM. We also present a method to improve the SEM's topological consistency by using corner constraints. The proposed algorithm has been verified in a simulation and an indoor experiment. The results show that the algorithm can localize the robot and build a complex map effectively. 相似文献