机器人同时定位与地图构建技术研究*

移动机器人同时定位与地图创建是实现未知环境下机器人自主导航的关键性技术,具有广泛的应用前景,也是目前机器人研究的热门课题之一。针对国内外近年来关于移动机器人同时定位与地图创建的研究工作进行了总结和分析,重点介绍了机器人的地图创建方法类别、基于概率理论的自主定位方法、同时定位与地图创建的问题描述及研究方法等方面的发展现状及存在的不足。     

Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots

This paper presents a novel method of integrating fuzzy logic (FL) and genetic algorithm (GA) to solve the simultaneous localization and mapping (SLAM) problem of mobile robots. The core of the proposed SLAM algorithm is based on an island model GA (IGA) which searches for the most probable map(s) such that the associated pose(s) provides the robot with the best localization information. Prior knowledge about the problem domain is transferred to GA in order to speed up the convergence. Fuzzy logic is employed to serve this purpose and allows the IGA to conduct the search starting from a potential region of the pose space. The underlying fuzzy mapping rules infer the uncertainty in the robot's location after executing a motion command and generate a sample-based prediction of its current position. This sample set is used as the initial population for the proposed IGA. Thus the GA-based search starts with adequate knowledge on the problem domain. The correspondence problem in SLAM is solved by exploiting the property of natural selection, which supports better performing individuals to survive in the competition. The proposed algorithm follows essentially no assumption about the environment and has the capacity to resolve the loop closure problem without maintaining explicit loop closure heuristics. The algorithm processes sensor data incrementally and therefore, has the capability of real time map generation. Experimental results in different indoor environments are presented to validate robustness of the algorithm.     

激光—相机系统语义栅格建图和路径规划

目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。     

一种低功耗程控滤波器的设计

针对目前移动机器人同时定位与地图创建(SLAM)研究中多采用激光雷达或超声环作为测距传感器导致系统复杂、成本高的问题,提出了一种利用舵机带动单超声传感器扫描的低成本设计方案。在高斯超声模型基础上,利用贝叶斯公式对栅格地图进行概率更新,并结合Sobel边缘检测算法提取特征点,实现了由不确定的移动机器人坐标系向固定的以环境特征点为原点的全局环境坐标系的转换及全局定位,为在相同环境下通过重复实验进行多地图融合研究奠定了基础。该低成本移动机器人设计的有效性通过实验得以验证。     

Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

移动机器人SLAMiDE系统设计与实现

针对动态环境下移动机器人同时定位与建图(SLAM)问题,设计一种动态环境SLAM系统——SLAMiDE,给出系统的整体结构框架和实现过程。该系统通过动态目标检测、SLAM和动态目标跟踪同时实现动态地图构建、静态地图构建和机器人定位。实验结果证明,该系统是正确、可行的。     

A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.     

基于ROS与Kinect的移动机器人同时定位与地图构建

同时定位与地图构建(SLAM)技术一直以来都是移动机器人实现自主导航和避障的核心问题,移动机器人需要借助传感器来探测周围的物体同时构建出相应区域的地图。由于传统的1D和2D传感器,如超声波传感器、声呐和激光测距仪等在建图过程中无法检测出Z轴(垂直方向)上的信息,易增加机器人发生碰撞的概率,同时影响建图结果的精确度。本文利用Kinect作为机器人SLAM的传感器,将其采集到的三维信息转化成二维的激光数据进行地图构建,同时借助机器人操作系统(robot operating system,ROS)进行仿真分析和实际测试。结果表明Kinect可以弥补1D和2D传感器采集信息的不足,同时能够较好的保持建图的完整性和可靠性,适用于室内的移动机器人SLAM实现。     

基于混合位姿估计模型的移动机器人三维地图创建方法

针对室内复杂环境下的稠密三维建模问题, 提出一种基于RGB-D 相机的移动机器人同时定位与三维地图创建方法. 该方法利用架设在移动机器人上的RGB-D 相机获取环境信息, 根据点云和纹理加权模型建立结合局部纹理约束的混合位姿估计方法, 确保定位精度的同时减小失败率. 在关键帧选取机制下, 结合视觉闭环检测方法, 运用树结构网络优化(TORO) 算法最小化闭环误差, 实现三维地图的全局一致性优化. 在室内环境下的实验结果验证了所提出算法的有效性和可行性.

     

基于视觉的移动机器人同时定位与建图研究进展

同时定位与建图(SLAM)是实现移动机器人真正自治的必要前提, 视觉传感器由于能够提供丰富的环境信息而在SLAM研究中受到重视, 本文从视觉传感器配置方式、视觉特征提取方法、视觉SLAM实现机制、地图表示类型以及环境对视觉SLAM的影响五个方面综述基于视觉传感器的同时定位与建图研究的发展现状, 对已有的典型视觉SLAM方法进行分析和比较, 并展望了未来的发展趋势.     

移动机器人即时定位与地图创建问题研究

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.􀁱 􀁽     

面向观测融合和吸引因子的多机器人主动SLAM

针对未知环境下多机器人主动SLAM(simultaneous localization and mapping)存在不能完全遍历环境、定位精度不理想等问题,本文基于EKF-SLAM(extended Kalman filter-simultaneous localization and mapping)算法提出一种多机器人主动SLAM算法。通过引入吸引因子,增强多机器人系统之间的交流,提升机器人自身定位精度与环境建图精度,同时又引导多机器人团队进行探索环境。当同一地标被多个机器人观测到,采用凸组合融合方法融合各个机器人对地标的估计,从而降低被估计地标的不确定度。仿真结果表明,所提算法能够对环境进行覆盖遍历,提升对地标估计的定位精度。     

移动机器人导航空间表示及SLAM问题研究

导航研究是移动机器人研究的承要领域之一。 空间表示则是移动机器人导航研究的基础性问题。围绕移动机器人导航空间表示，该文首先对目前广泛采用的空间分解表示，几何特征表示，拓扑地图表示等多种移动机器人导航空间表示方法进行详细的归纳和总结。通过对移动机器人导航空间各种表示疗法进行性能对比，指出各种空间表示方法的优点与不足。最后，对移动机器人导航研究中的同时定位与地图创建（SLAM）问题作了阐述，指出SLAM研究面临的问题，探讨了SLAM的未来研究方向。     

两种基于激光雷达的SLAM算法最优参数分析

激光雷达是移动机器人同步定位与地图构建(SLAM)的重要模块.对目前主流的基于激光雷达的SLAM方法(Gmapping和Hector SLAM)进行研究,借助开源机器人操作系统(ROS),在自主研发的移动机器人平台上配备激光雷达,实现了不同参数配置下两种算法的地图构建.实验为参数最优配置指明了方向,且证明了Hector SLAM的整体构图精度高于Gmapping,但对参数配置要求较高.     

基于扫描匹配的室外环境SLAM 方法

针对室外环境中的机器人“绑架”问题,提出了基于地图匹配的SLAM方法．该方法舍弃了机器人里程计信息, 只利用局部地图和全局地图的图形相关性进行机器人定位．方法的核心是多重估计数据关联,并将奇异值分解应用到机器人位姿计算中．利用Victoria Park数据集将本算法与基于扩展卡尔曼滤波器的方法进行比较,实验结果证明了本文提出的算法的有效性．     

基于协助校正方法的多机器人主动同时定位与建图

协作策略是多机器人主动同时定位与建图(SLAM)的关键。文中提出一种多机器人相互校正的协作策略, 称为协助校正。 该方法通过优化机器人对陆标的观测来提高定位与建图的精度, 共包括弱协助校正和强协助校正两种模式。 前者是一种间接的协助模式, 可应用于所有机器人自身定位均不准确的情形。 后者是一种直接的协助模式, 由自身定位精度较高的机器人主动校正其它机器人及相应陆标。 文中将这两种协助校正模式利用状态机统一到多机器人主动SLAM应用中。在仿真实验中将协助校正与其它多机器人主动SLAM方法进行对比以验证其精度优势, 并与单机器人主动SLAM对比以验证其导航代价极低的优势。最后在两台Poineer3-DX移动机器人上进行真实环境实验,实验结果证实协助校正方法可在实际应用中有效提高多机器人主动SLAM的探索效率和精度。     

Simultaneous localization and mapping using the Geometric Projection Filter and correspondence graph matching

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks are set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved using correspondence graphs. Secondly, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the previous approaches. Experimental results are presented using an outdoor robot car equipped with a two-dimensional scanning laser sensor.     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

Sonar Sensor-Based Efficient Exploration Method Using Sonar Salient Features and Several Gains

This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a ??sonar salient feature?? (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments.