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.     

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.􀁱 􀁽     

Large scale graph-based SLAM using aerial images as prior information

The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.     

VecSLAM: An Efficient Vector-Based SLAM Algorithm for Indoor Environments

In this paper, we present an efficient SLAM (Simultaneous Localization and Mapping) algorithm named VecSLAM, which localizes and builds a vector map for mobile robots in indoor environments. Compared to grid-mapping approaches, vector-based mapping algorithms require a relatively small amount of memory. Two essential operations for successful vector mapping are vector merging and loop closing. Merging methods used by traditional line segment-based mapping algorithms do not consider the sensor characteristics, which causes additional mapping error and makes it harder to close loops after navigation over a long distance. In addition, few line segment-based SLAM approaches contain loop closing methodology. We present a novel vector merging scheme based on a recursive least square estimation for robust mapping. An efficient loop closing method is also proposed, which effectively distributes the resultant mapping error throughout the loop to guarantee global map consistency. Simulation studies and experimental results show that VecSLAM is an efficient and robust online localization and mapping algorithm.     

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments     

面向地图构建的移动机器人局部路径自主规划

移动机器人在未知场景中规划路径以自主完成定位与地图构建是机器人领域的一个重要研究课题.本文阐述了一种利用实时构建的信息熵地图动态生成机器人的局部探索路径,并综合转向约束和避障约束设计了一种基于模糊评价方法的方向选择策略跟踪生成的局部路径并进行环境构图.与现有方法相比,本文方法能够根据环境动态地生成平滑连续的局部探索路径,并能引导机器人进行障碍物躲避和完成自主构图.实验结果表明相较对比方法,本文方法的探索路程最短,观测覆盖度最高,同时整个自主构图过程所需的时间也更短.     

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.     

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.     

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。     

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。     

Hybrid robot control and SLAM for persistent navigation and mapping

For a mobile robot to operate autonomously in real-world environments, it must have an effective control system and a navigation system capable of providing robust localization, path planning and path execution. In this paper we describe work investigating synergies between mapping and control systems. We have integrated development of a control system for navigating mobile robots and a robot SLAM system. The control system is hybrid in nature and tightly coupled with the SLAM system; it uses a combination of high and low level deliberative and reactive control processes to perform obstacle avoidance, exploration, global navigation and recharging, and draws upon the map learning and localization capabilities of the SLAM system. The effectiveness of this hybrid, multi-level approach was evaluated in the context of a delivery robot scenario. Over a period of two weeks the robot performed 1143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), travelled a total distance of more than 40 km, and recharged autonomously a total of 23 times. In this paper we describe the combined control and SLAM system and discuss insights gained from its successful application in a real-world context.     

基于粒子滤波的无线传感器网络辅助同步定位与地图创建方法研究

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．     

基于MIEKF的移动机器人同时定位与地图构建研究*

由于移动机器人处在未知并且不确定的环境中,主要采用基于概率的方法对同时定位与地图构建(SLAM)进行描述。本文建立了SLAM问题的概率表示模型,并对在解决SLAM问题中用最常用的扩展卡尔曼滤波(EKF)算法以及迭代扩展卡尔曼滤波(IEKF)算法进行描述。本文针对两种算法的缺陷和不足,将应用于跟踪领域的修正迭代扩展卡尔曼滤波算法(MIEKF)与SLAM思想结合,提出了一种新的基于MIEKF的SLAM算法。通过基于点特征的SLAM实验验证了该算法的有效性。     

基于视觉的同时定位与地图构建方法综述

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。     

SLAM 问题中机器人定位误差分析与控制

移动机器人同步定位与建图问题 (Simultaneous localization and mapping, SLAM) 是机器人能否在未知环境中实现完全自主的关键问题之一. 其中, 机器人定位估计对于保持地图的一致性非常重要. 本文分析了 SLAM 问题中机器人定位误差的收敛特性. 分析表明随着机器人的运动,机器人定位误差总体上逐渐增大; 在完全未知环境中无法预测机器人定位误差的上限. 根据理论分析, 本文提出了一种控制机器人定位误差在单位距离上增长速度的算法. 该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度, 从而控制机器人定位误差的增长.     

Robot Tracking in SLAM with Masreliez-Martin Unscented Kalman Filter

International Journal of Control, Automation and Systems - The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, a robot tracking algorithm in...     

Least-square Matching for Mobile Robot SLAM Based on Line-segment Model

International Journal of Control, Automation and Systems - This study proposes an efficient simultaneous localization and mapping (SLAM) algorithm for a mobile robot. The proposed algorithm...     

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.     

非结构化场景下移动机器人FastSLAM应用研究

FastSLAM算法是当前移动机器人自定位与自建地图领域中研究的热点和关键。系统论述FastSLAM关键技术及基本理论,并设计非结构化场景进行自定位与自建地图应用研究。首先,对贝叶斯滤波理论进行了概述,得到移动机器人SLAM问题的基本贝叶斯滤波递归形式;其次,应用Rao—BlackweUised理论将状态分解为采样部分和解析部分进行讨论,得到SLAM问题的解耦形式;再次,论述了算法中序贯和综合重采样粒子滤波器;最后给出FastSLAM伪算法的整体实现过程,给出在非结构化场景下仿真结果。仿真结果表明FastSLAM算法在非结构化场景下能够有效快速地实现自定位与地图创建,当取粒子数合适时,在快速性和精确性方面都能够达到理想效果。     

Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM

This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this paper apart from previous ones: 1) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem and 2) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than in previous works. We also describe a practical implementation that aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30 000 m², a 2 km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.