一种同时定位与地图构建的仿真系统

基于近年来流行的移动机器人同时定位与地图构建的方法,提出了一种仿真系统,详细地描述了系统的定义模型结构和运行方式流程。系统中采用了一种基于线性特征的环境地图描述方法。仿真系统中算法设计独立,扩充性好,仿真功能强大,方便了机器人定位算法的研究,为算法的实际运用奠定了基础。     

基于超声波的移动机器人的同时定位和地图构建

为了在移动机器人SLAM过程中得到更精确的定位和二维地图构建,对一种利用超声波传感器信息进行栅格地图创建的方法提出了改进;该方法利用Bayes法则对信息进行融合,利用粒子滤波和航位推算相结合的方法对机器人进行精确定位和创建地图,然后利用移动的栅格法进行地图的全局更新,提出了一种地图的校验方法;通过实验,在粒子数为200的情况下分别得到了算法改进前和改进后的地图构建结果,通过比较,证明了使用该算法进行移动机器人定位和地图构建更加精确。     

Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data

Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.     

一种新的粒子滤波SLAM算法

粒子滤波SLAM算法的复杂度与特征个数呈线性关系,对于大规模SLAM有明显的计算优势,但是这些算法不能长时间满足一致性要求.将边缘粒子滤波技术(marginal particle filtering,MPF)运用到SLAM技术中,并利用Unscented Kalman滤波(UKF)来计算提议分布,得到了一种新的粒子滤波SLAM算法.新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求.该算法克服了一般粒子滤波SLAM算法的缺点,性能优势十分明显.     

一种基于特征地图的移动机器人SLAM方案

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.     

Map Management for Efficient Simultaneous Localization and Mapping (SLAM)

The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is now well understood. Although a number of SLAM implementations have appeared in the recent literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than a few tens of landmarks. This paper presents the theoretical basis and a practical implementation of a feature selection strategy that significantly reduces the computation requirements for SLAM. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map building process statistically inconsistent. Furthermore, it is shown that the computational cost of the SLAM algorithm can be reduced by judicious selection of landmarks to be preserved in the map.     

Path Planning for Improved Visibility Using a Probabilistic Road Map

This paper focuses on the challenges of vision-based motion planning for industrial manipulators. Our approach is aimed at planning paths that are within the sensing and actuation limits of industrial hardware and software. Building on recent advances in path planning, our planner augments probabilistic road maps with vision-based constraints. The resulting planner finds collision-free paths that simultaneously avoid occlusions of an image target and keep the target within the field of view of the camera. The planner can be applied to eye-in-hand visual-target-tracking tasks for manipulators that use point-to-point commands with interpolated joint motion.      

嵌入式移动机器人视觉定位及地图构建系统设计

设计了一种具有定位和导航功能的嵌入式移动机器人,采用双控制器协同工作模式并具有多种感知模块;在设计的嵌入式平台上进行了单目视觉定位和导航研究;通过彩色路标和电子罗盘实现对机器人的定位,分析了摄像机成像原理,给出了世界坐标系和图像坐标系的映射关系,简化了机器人定位的难度;通过超声波传感器旋转测距获得周围环境信息,对环境信息处理后建立地图的栅格模型;实验表明该定位方法能够准确提取路标的重心,具有较好的定位精度,减少了计算时间;通过超声波数据可以比较准确的建立环境模型,能够满足避障要求。     

A Nonlinear Set Membership Approach for the Localization and Map Building of Underwater Robots

This paper proposes a set membership method based on interval analysis to solve the simultaneous localization and map building (SLAM) problem. The principle of the approach is to cast the SLAM problem into a constraint satisfaction problem for which interval propagation algorithms are particularly powerful. The resulting propagation method is illustrated on the localization and map building of an actual underwater robot.      

Efficient Simultaneous Localization and Mapping Based on Ceiling-View: Ceiling Boundary Feature Map Approach

For simultaneous localization and mapping (SLAM) based on the extended Kalman filter, the size of the state vector is an essential factor because the feasibility depends on it. In this paper, a new SLAM based on ceiling vision (cv-SLAM) is proposed. To keep the size of the state vector compact, the boundaries between ceiling and walls are used as landmarks for visual SLAM (vSLAM). The ceiling boundaries are robust to illuminative variations and they are not as numerous as the point features. Some constraints are imposed on the features based on the characteristics of the ceiling boundaries and an efficient update method called 'double update' is proposed to improve the SLAM performance. The basic idea of the double update is to fully utilize the intersections of the boundary features. Finally, the proposed SLAM is applied to some simulations and experiment, and its effectiveness is demonstrated through them.     

Using Cellular Probabilistic Self-Organizing Map in Borrowing Channel Assignment for Patterned Traffic Load

The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques. Borrowing channel assignment (BCA) is a compromising technique between fixed channel allocation (FCA) and dynamic channel allocation (DCA). However, in the case of patterned traffic load, BCA is not efficient to further enhance the performance because some heavy-traffic cells are unable to borrow channels from neighboring cells that do not have unused nominal channels. The performance of the whole system can be raised if the short-term traffic load can be predicted and the nominal channels can be re-assigned for all cells. This paper describes an improved BCA scheme using traffic load prediction. The prediction is obtained by using the short-term forecasting ability of cellular probabilistic self-organizing map (CPSOM). This paper shows that the proposed CPSOM-based BCA method is able to enhance the performance of patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method delivers significantly better performance than BCA for patterned traffic load situations, and is virtually as good as BCA in the other situations analyzed.     

Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.     

Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array

Sound source localization is an important function in robot audition. Most existing works perform sound source localization using static microphone arrays. This work proposes a framework that simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot. First, an eigenstructure-based generalized cross-correlation method for estimating time delays between microphones under multi-source environments is described. Using the estimated time delays, a method to compute the farfield source directions as well as the speed of sound is proposed. In addition, the correctness of the sound speed estimate is utilized to eliminate spurious sources, which greatly enhances the robustness of sound source detection. The arrival angles of the detected sound sources are used as observations in a bearing-only simultaneous localization and mapping procedure. As the source signals are not persistent and there is no identification of the signal content, data association is unknown and it is solved using the FastSLAM algorithm. The experimental results demonstrate the effectiveness of the proposed method.     

Real Time Map Building by Means of an Ellipse Spatial Criterion and Sensor-Based Localization for Mobile Robot

In the present paper, map building and localization problems are examined. A new algorithm is proposed for each task. The map building algorithm is based on measurements derived by ultrasonic sensors. It has small memory requirements and can distinguish between parallel edges and edges-corners. The discrimination between edges and corners is achieved based on the physical restrictions imposed by the ultrasonic sensors and the way they are fired. The parallel edges discrimination is carried out on the basis of an ellipse spatial criterion. It is of low computational complexity and, therefore, can be applied on line. The application of the proposed algorithm does not require tracking of a continuous path. The method is accurate. It converges to the existing map characteristics. The mean inclination error is equal to 0.78 degrees while the mean distance error of the mid point of the chartographed edges from the actual edges is equal to 4.11 cm. The robustness of the algorithm was verified by applying it in noisy environments. The localization algorithm reduces the accumulated odometry error by utilizing readings obtained from ultrasonic sensors. Its application does not require a priori knowledge of the statistical characteristics of the noise that affects the measurements, nor the exact robot starting position. It is of low computational complexity. The application of the method was tested on a track with length equal to 25.85 m. The experiment was repeated 50 times. The mean position error was equal to 26 cm, while a dramatic reduction of the mean position error was achieved in regions for which the detected obstacles were parallel to only one of the reference coordinate axes. The mean error in such regions was reduced to 30 cm from 48 cm. The mean heading error was equal to 6.8°.     

Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database

A framework is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D map with building outlines with neither 3D geometric information nor appearance data. The proposed method firstly reconstructs a set of vertical planes by sampling and clustering vertical lines from the image with random sample consensus (RANSAC), using the derived 1D homographies to inform the planar model. Then, an optimal autonomous localization algorithm based on the 2D building boundary map is proposed. The physical experiments are carried out to validate the robustness and accuracy of our localization approach.     

Segment-Based Map Building Using Enhanced Adaptive Fuzzy Clustering Algorithm for Mobile Robot Applications

In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.     

基于信任知识库的概率模糊认知图

模糊认知图较难表示概念间因果关系测度的不确定性、因果联系的时空特性及专家对知识的不确定性．在继承模糊认知图模型优点的前提下,在概念间的因果关系中引入条件概率及信任知识库表示,提出基于信任知识库的概率模糊认知图模型．该模型用条件概率及信任知识库表示因果联系的时空特性、专家对知识及概念间因果关系测度的不确定性,从而将因果关系测度的不确定性、因果联系的时空特性及专家对知识的不确定性有效地融入模糊认知图中,自然扩展了模糊认知图模拟因果关系的能力,较大限度地减少了认知图对现实世界模拟的失真．最后通过实验说明了基于信任知识库的概率模糊认知图模型,具有比FCM更强的模拟能力．     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.     

A Discussion of Simultaneous Localization and Mapping

This paper aims at a discussion of the structure of the SLAM problem. The analysis is not strictly formal but based both on informal studies and mathematical derivation. The first part highlights the structure of uncertainty of an estimated map with the key result being “Certainty of Relations despite Uncertainty of Positions”. A formal proof for approximate sparsity of so-called information matrices occurring in SLAM is sketched. It supports the above mentioned characterization and provides a foundation for algorithms based on sparse information matrices. Further, issues of nonlinearity and the duality between information and covariance matrices are discussed and related to common methods for solving SLAM. Finally, three requirements concerning map quality, storage space and computation time an ideal SLAM solution should have are proposed. The current state of the art is discussed with respect to these requirements including a formal specification of the term “map quality”. This article is based on research conducted during the author's Ph.D. studies at the German Aerospace Center (DLR) in Oberpfaffenhofen. Udo Frese was born in Minden, Germany in 1972. He received the Diploma degree in computer science from the University of Paderborn in 1997. From 1998 to 2003 he was a Ph.D. student at the German Aerospace Center in Oberpfaffenhofen. In 2004 he joined University of Bremen. His research interests are mobile robotic, simultaneous localization and mapping and computer vision.