Localization for Mobile Robot Teams A Maximum Likelihood Approach

移动机器人定位方法概述摘要：介绍了当前自主移动机器人的定位方法研究现状，对相对定位和绝对定位做了概述，对绝对定位中主要的研究方法做了介绍，并对概率机器人学所采用的主要定位方法做了介绍。

关键词：移动机器人；相对定位；绝对定位；概率机器人学0 引言随着工业自动化的发展，生产加工的自动化程度越来越高。

机器人技术的出现和发展使传统的工业生产面貌发生根本性的变化。

移动机器人的定位是其执行其他任务的前提和基础，也是评价机器人性能的关键指标之一。

移动机器人定位是指机器人通过感知获取环境信息，经过相关的信息处理而确定自身及目标位姿的过程。

自主移动机器人导航过程需要回答3个问题：“我在哪里？”，“我要去哪里？”和“我怎样到达那里？”。

移动机器人定位技术就是要解决第1个问题。

准确来说，移动机器人定位的目的就是确定机器人在其运动环境中的世界坐标系的坐标。

根据机器人定位过程，可分为相对定位和绝对定位。

但在机器人定位过程中，单独地使用其中任何一个定位方式都不能很好地解决移动机器人的定位问题。

因而，在目前的定位技术中主要是将两者结合在一起，完成对移动机器人定位。

本文对相对定位技术和绝对定位技术分别进行概述。

1 移动机器人相对定位研究移动机器人的相对定位也称作位姿跟踪。

假定机器人的初始位姿，采用相邻时刻的传感器信息对机器人的位置进行跟踪估计。

相对定位法分为里程计法和惯性导航法。

1.1 里程计法（Odometry）在机器人的导航技术中，里程计法是使用最为广泛的定位方法。

在移动机器人的车轮上装有光电编码器，通过对车轮转动的记录来实现对机器人的位姿跟踪。

在位置跟踪中，机器人的当前位置是根据对它的以前的位置的知识而更新的，而这需要假定机器人的初始位置已知，并且机器人姿态的不确定性必须小。

通过这种方式来实现机器人定位的方法也成为航位推算法。

航位推算是一个累加过程，在这个逐步累加的过程中，测量值以及计算值都会累积误差，使得定位精度不断下降。

本科毕业设计题目：移动机器人FastSLAM算法研究学院:专业:学号:学生姓名:指导教师:日期:摘要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。

基于卡尔曼滤波器的SLAM算法有计算的复杂性以及对数据融合误差非常敏感的缺点，使其不能再实际环境中得到广泛应用。

为解决传统SLAM算法的缺陷，介绍了一种基于Rao-Blackwellized粒子滤波器的FastSLAM方法，该方法将SLAM问题分解为对机器人姿态和路标在地图中的位置的递归算法。

每一粒粒子都有对应的地图，再将地图估计分解成N个独立的特征估计，路径估计采用粒子滤波器，地图估计采用扩展卡尔曼滤波器。

FastSLAM有机地将粒子滤波器与卡尔曼滤波器集成在一起，鲁棒性地解决数据关联和多目标跟踪问题，其时间消耗与路标的数量成对数关系，计算量小，用时短。

基于Rao-Blackwellized粒子滤波器的FastSLAM算法是一种高效的机器人同步定位和绘制地图的算法，其具有高效性和准确性，该方法使用提高了机器人地图创建的实时性，增强了避障能力。

关键词：移动机器人；FastSLAM算法；路径估计；地图估计AbstractMobile robot localization and mapping the key technologies of the robot autonomous navigation in unknown environment, has broad application prospects, is currently a hot research topic of the robot. The Kalman filter-based SLAM algorithm to calculate the complexity and the error is very sensitive to the shortcomings of the data fusion, so that it can not be widely applied in the actual environment.FastSLAM based on Rao-Blackwellized particle filter SLAM problem is decomposed into the position of the robot pose and landmarks on the map recursive algorithm to solve the defects of the traditional SLAM algorithm. A particle has a corresponding map, and then map the estimated decomposed into N independent characteristics estimated path estimation using particle filter, map estimated using extended Kalman filter.The FastSLAM organic particle filter and Kalman filter integrated, robust solution to data association and multi-target tracking, the time consumption and the number of landmarks logarithmic small amount of calculation, with short.FastSLAM algorithms based on Rao-Blackwellized particle filter is an efficient robot simultaneous localization and mapping algorithm, its efficiency and accuracy, the method to improve the robot map created real-time, and enhance the ability to obstacle avoidance.Key words: Mobile robot; The FastSLAM algorithm; The path estimation;The map estimation目录1 绪论 (1)1.1 移动机器人定位和地图创建问题 (2)1.1.1 移动机器人国内外发展状况 (2)1.1.2 移动机器人的地图构建问题 (3)1.1.3 机器人的定位方法 (5)2 基于粒子滤波器的SLAM算法 (7)2.1 SLAM的通用框架和理论模型 (7)2.2 粒子滤波器定位的基本原理 (8)2.3 扩展卡尔曼滤波器算法 (9)2.3 粒子重采样 (10)2.4 移动机器人SLAM问题描述 (10)2.4.1 SLAM计算复杂度 (10)2.4.2 SLAM的联合估计 (11)2.4.3 SLAM的后验估计表示 (11)2.4.4 SLAM公式推导 (13)2.4.5 有效的数据关联 (14)2.4.6 FastSLAM的粒子表示形式 (15)2.4.7 FastSLAM的计算时间复杂度 (16)3 模型建立 (17)3.1 运动模型 (17)3.2 观测模型 (18)4 FastSLAM算法步骤 (19)4.1 FastSLAM算法步骤 (19)4.2 新位姿采样 (20)4.3 环境特征估计的更新 (20)5算法流程图和代码 (21)5.1 FastSLAM算法伪代码 (21)5.2 FastSLAM算法流程 (21)6 仿真环境建立和仿真结果 (23)6.1 仿真环境介绍 (23)6.2 仿真结果 (23)7 总结和展望 (28)参考文献 (29)致谢 (30)1 绪论移动机器人的同时定位与地图创建(SLAM)问题有重要的理论与应用价值，被很多学者认为是实现真正全自主移动机器人的关键。

邢远见, 王诗淇, 王 冠(哈尔滨工业大学 计算机科学与技术学院，哈尔滨 150001) E-mail:xyjhit@摘 要：本文通过激光传感器采集数据，结合现代时间序列分析的方法给出了同时定位和地图生成(SLAM)的算法。

在噪声统计未知情况下基于辨识ARMA 新息模型得到的自校正滤波器，滤去噪声数据，与传感数据和环境特征相匹配，使得机器人位置连续更新。

仿真表明该方法在噪声统计未知情况下滤波具有有效性，较好的解决了室内机器人导航问题。

关键词：同时定位和地图生1成；现代时间序列分析；ARMA信息模型；自校正滤波器；室内机器人中图法分类号：TP24 文献标识码: A1. 前言未知环境中机器人的同时定位与地图生成（SLAM）问题是自主机器人应用面临的挑战之一[1]。

同时定位与地图创建问题，即全自主机器人在未知环境中进行运动时，依赖传感器的信息进行建模，并利用所创建的地图环境估测位姿。

目前应用的传感器有超声波传感器，全维立体视觉[2]，毫米波雷达技术[3]，激光测距器[4,5]等。

其中激光测距器由于在距离范围和方向上有较高的精确度，而且成本较毫米波雷达技术低，计算复杂性较好等优点，已经成为全自主机器人室内定位和地图生成最有应用价值的传感器之一。

目前SLAM 问题的解决方法可以分为两类[6]-[16]：一类为基于概率模型的方法，另一类为基于非概率模型方法。

许多基于卡尔曼滤波的方法如完全SLAM 、压缩滤波、FastSLAM 就属于概率模型方法。

非概率模型方法有SM-SLAM 、扫描匹配、数据融合(data association)、基于模糊逻辑等。

现代时间序列分析[17]是基于概率模型的一种解决SLAM 问题的方法。

基于这种观点的提出的自校正滤波器在噪声统计未知，或部分已知，或近似已知的情况下，可改善和提高滤波精度。

本文即采用激光传感器，结合时间序列分析的方法来解决SLAM 问题。

2. 同时定位和地图生成同时定位和地图生成（SLAM ）问题包括：1) 如何进行环境描述，即环境地图的表示方法；2) 怎样获得环境信息，机器人在环境中漫游并记录传感器的感知数据，这涉及到机器人的定位与环境特征提取问题；3) 如何表示获得的环境信息，并根据环境信息更新地图，这需要解决对不确定信息的描述和处理方法。

Metric Localization Using a Single Artificial Landmarkfor Indoor Mobile RobotsGijeong Jang, Sungho Kim, Jeongho Kim and Inso KweonDepartment of Electrical Engineering & Computer ScienceKorea Advanced Institute of Science and Technology (KAIST)373-1, Guseong-dong, Yuseong-gu, Daejeon, Koreagjjang@rcv.kaist.ac.kr, shkim@rcv.kaist.ac.kr, jhkim@rcv.kaist.ac.kr, iskweon@kaist.ac.krAbstract – We present an accurate metric localization method using a simple artificial landmark for the navigation of indoor mobile robots. The proposed landmark model is designed to have a three-dimensional, multi-colored structure and the projective distortion of the structure encodes the distance and heading of the robot with respect to the landmark. Catadioptric vision is adopted for the robust and easier acquisition of the bearing measurements for the landmark. We propose a practical EKF based self-localization method that uses a single artificial landmark and runs in real time.Index Terms – metric localization, artificial landmark, catadioptric vision, EKFI.I NTRODUCTIONMany vision-based self-localization methods using artificial or natural landmarks have been proposed. Natural landmarks are appropriate for both indoor and outdoor environments. They are selected considering their geometrical or photometrical features. Mobile robots analyze their characteristics and find self-pose using them [1] [2]. However, it is a formidable task to extract consistent and reliable landmarks in a complex environment. On the other hand, an artificial landmark is a very simple and powerful tool for self-localization in indoor environments [3]. Advantages of landmarks based on specific patterns include easier detection, identification, and tracking [4]. For example, self-similar gray pattern landmarks are used as navigation and localization aids [5].The commonly used method for computing the pose estimates of the robot is based on the bearing measurements of more than three landmarks [6]. The general approach for the method starts from detecting, identifying, and tracking multiple landmarks spread over the workspace [7]. To mitigate the difficulty of managing multiple landmarks, some approaches employ a single landmark of a plane pattern [1] [8] [9]. The projective distortion of the pattern makes it possible to calculate the relative pose of the robot from the pattern. However, the sensitivity of projective distortion relative to the change of viewing direction is severely reduced from the frontal view. For example, a change of D10 in viewing direction from the frontal view introduces a projective distortion of only 1.5%.In this paper, we explore a global and incremental localization method for a mobile robot working in an indoor environment. We propose a simple artificial landmark model and a practical localization algorithm that uses bearing measurements of a single landmark. The proposed geometric beacon is a special type of target that can be reliably observed in successive sensor measurements and that can be accurately described in concise geometric terms. The novel 3-D structure of the pattern makes it possible to accomplish accurate single view-based localization in real time.II.L ANDMARK M ODELFig. 1 shows the appearance of the proposed landmark. The color pattern is composed of two horizontally neighboring color patches. The color pattern subtends an angle of D45 with respect to two supporting planes. The two supporting planes intersect at the right angle and form a black line. The relative position of the line with respect to the color pattern varies as a function of camera orientation.Fig. 1 Structure of LandmarkThe brightness variation caused by the illuminant change is reduced by the use of the chromaticity color space:[])/()/(][BGRBBGRRbr++++= (1) To make each landmark distinguishable from the environment, multiple colors that are distant in the chromaticity color space are selected.2005 IEEE/RSJ International Conference on Intelligent Robots and SystemsIII.L ANDMARK D ETECTION AND T RACKING The catadioptric vision sensor (omnidirectional camera) has a very wide field of view [10]. Another advantage of this camera is that it provides direct reports on horizontal bearing measurements. These facts simplify the problem of bearing-only localization.As shown in Fig. 2, if the surface of the mirror is formed by revolving a hyperbola around the Z-axis, all the rays directed to the focal point of the mirror reflect on the mirror surface and turn to the principal point of the lens.Consequently, if a robot moves on a flat plane, the patterns on the height Z invariably appear on the same radius in the omnidirectional image as shown in Fig. 3. The black circle is the horizontal line that is the projection of the horizontal plane in the omnidirectional image. As the robot begins to move and takes an image sequence, the points on the horizontal plane move only along the horizontal line.Fig. 2 Configurations of omnidirectional cameraFig. 3 Horizontal line for omnidirectional imageFig. 4 is a special case of an omnidirectional spatiotemporal image, an Omnidirectional Route Panorama (ORP), which is the sequential stacking of horizontal lines taken by the robot moving on a plane [11].A simple edge operation with nonmaxima suppression on ORP makes feature point tracking extremely easy. For example, a Canny edge operation [12] finds the edge trace. This means that the data association problem in localization can be solved simply by tracing the edge of ORP. In addition, memory can be saved for scene description and real-time edge tracking is feasible.Because of the wide field of view, the landmark can be detected in any pose of the vehicle. Here, a simple correlation-based detector is used to detect the angular range of the color pattern with the bearing of the rear vertical black line in thehorizontal line.Fig. 4 Omnidirectional route panoramaIV.L OCALIZATION A LGORITHMIn this section, we present an algorithm that allows the robot to verify its absolute position from a view of a single landmark in one image. Based on the assumption that an indoor mobile robot navigates on a flat floor, we only seek the robot’s two-dimensional location and orientation with respect to the landmark.We assume that the origin of the reference coordinate is set as the location of the landmark in the 2-D floor map for localization of the mobile robot. We also assume that the landmark is positioned on the level of the horizontal plane of the omnidirectional mirror.In previous approaches based on a single landmark with a plane pattern, the sensitivity of projective distortion relative to the change of viewing direction is very low from the frontal view.Assuming orthographic projection [13] and a small viewing angle, Fig. 5 shows the projection model of a plane pattern.Fig. 5 Configuration of projection of plane landmark (top view)The relation between α and θ isαcos⋅=′ll (2)αθcos⋅⋅≈lk):(constk (3) frameazimuthααθsin ⋅⋅−≈l k d d . (4)From (4), we observe that the sensitivity of observationangle θwith respect to viewing direction α becomes very small from the frontal view when α is small.Localization by observing three known points can be accomplished simply by solving trigonometric equations. However, this method is inaccurate in the degenerative position. The modified version of the method [6] solves the problem and leads to an accurate solution. However, this is also a batch method that assumes very high measurement accuracy. For this reason, it is not adequate for robot applications that require real-time performance. The proposed localization method is based on Extended Kalman Filtering (EKF), which is a compact and fast state estimation algorithm for real-time applications.Fig. 6 Geometric configuration of the vehicle-landmark systemFig. 6 shows the geometric relation between the proposed landmark and the vehicle pose. With the help of the catadioptric vision sensor, we obtain three bearings of a point 1P , 2P , 3P .We denote the position and orientation of the vehicle by the state vectorT v v y x ),,(φ=sand the error covariance of the vehicle state by⎟⎟⎟⎠⎞⎜⎜⎜⎝⎛=φφφφφφP P P P P P P P P P yxy yy yxx xy xxThe measurement vector is composed of the measured bearings of the three points in the artificial landmarkTt ),,(321θθθ=zUsing this relatively simple model of the system and sequential measurements of three bearings, we estimate the pose of the robot represented by the state s and the uncertainty of the pose represented by the covariance P .A. Initial Pose CalculationEKF requires initialization of the state. To find the initial state, we calculate the pose of the vehicle with measurements for three known points. However, the equations generated from the geometric relations are liable to fail in the degenerate configuration. For this reason, the initial pose of the robot is calculated by applying simple trigonometry and some feasible assumptions. It is relatively accurate. Moreover, EKF will improve the accuracy gradually in the next step.With the proposed configuration, the depth variation of the landmark itself is much smaller than the distance between the camera and the landmarks. In addition, the portion of the viewing angle for the landmark is small at a reasonable distance. From these observations, we assume an orthographic projection model and an orthoperspective projection model, which has the characteristic of parallel projection and parallel to projective projection, as shown in Fig. 7 [13].Fig. 7 Geometric configuration for initial pose calculationThe vehicle location is represented in polar coordinates by the distance from the origin d and the angle αas shown in Fig. 6. As auxiliary lines, we set two parallel lines—1l and 2l that are orthogonal to the viewing direction to the origin from the vehicle: 1l intersects the origin 2P and 2l intersects the center point of 1P and 3P . 1dis the distance between 1l and 2l , and 2d is the distance between the vehicle and 2l . Assuming orthographic projection and a small viewing angle⎟⎟⎠⎞⎜⎜⎝⎛−−=−23121tan θθθθα (5)Assuming orthoperspective projection21d d d += (6)2)sin (cos 1αα+=p d (7))()sin (cos 132θθαα−+=p d (8)From the observation 2θ and the calculated value dand α, the vehicle state T v v y x ),,(φ=s can be found as follows:αcos d x v = (9)αsin d y v = (10)2θαπφ−+= (11)B. I ncremental localizationGiven the previously estimated pose of the robot with a large initial error covariance, EKF gradually updates the vehicle pose by alternating the two phases – motion update and measurement update.1) Motion Update :The motion model for a differential-drive vehicle follows a nonlinear dynamics model, which is represented by a nonlinear function:),,(111−−−=t t t t f w u s s (12)where u denotes motion control for change of steering angle and moving distance and w is the corresponding noise. We assume this noise follows a zero mean Gaussian distribution. The robot’s motion in each time step is modeled as a simple form:)()2/)(cos(,1,1,2,211,,t t t t t t v t v w u w u x x +⋅+++=−−φ (13))()2/)(sin(,1,1,2,211,,t t t t t t v t v w u w u y y +⋅+++=−−φ (14))(,2,21t t t t w u ++=−φφ (15)Temporal update using EKF requires prediction of thestate and the covariance.First, we project the state ahead according to the motion model assuming no additive motion error.),,ˆ(ˆ110u s s−−−=t t t f (16)Second, we project the error covariance ahead.T t t t T t t t t W Q W A P A P 11−−−+= (17)Because the system is nonlinear, we should linearize thesystem dynamics at each given step. Linearization is accomplished by calculating the Jacobian matrix for the state transition:),,ˆ(11][][],[0u ss −−∂∂=t t j i j i f A (18)),,ˆ(11][][],[0u sw −−∂∂=t t j i j i f W (19)We let the process noise covariance Q (for moving distance and rotation angle) be diagonal and constant. The magnitude is set by observing the variance of unmodeled effects such as wheel slippage.2) Measurement Update :The measurement model for the angle measurements is the function of vehicle state and measurement noise:),(t t t h v s z = t v : measurement noise (20)The feature points in the artificial landmark are transformed from the world coordinates to the vehicle coordinates (Fig. 8). Setting a vehicle pose, the predicted measurement angle is calculated.Fig. 8 illustration for measurement modelThe position of the landmark in the robot frame is)(WW i RW R i R x y y −= (21)Where, W x is the vehicle position in the world frame and RWR is the rotation matrix changing the frame from world to robot. That is⎟⎟⎠⎞⎜⎜⎝⎛−−⎟⎟⎠⎞⎜⎜⎝⎛−=⎟⎟⎠⎞⎜⎜⎝⎛′′v v y y x x y x φφφφcos sin sin cos (22) Therefore, the measurement angle is()x y m ′′=−/tan 1θ (23)Linearization is accomplished by calculating the Jacobian matrix for the measurement matrix:),ˆ(][][],[0ss −∂∂=t j i j i h H (24)In the measurement update stage, we compute the Kalmangain by1)(−−−+=t T t t t T t t t R H P H H P K (25)The noise covariance t R of the measurements is determined by considering the image resolution. In (25), )(t T t t t R H P H +− means the projected uncertainty in the measurement space. The state is updated by weighting between the measured and predicted state as)),ˆ((ˆˆ0s z s s−−−+=t t t t t h K (26)The Kalman gain K is set to minimize the a posterioriestimate error covariance. Finally, we update the error covariance as−−=t t t t P H K I P )( (27)This alternating recursive algorithm provides the smoothing effect for process and measurement noise. It enhances state estimation accuracy and informs uncertainty of the estimated state, which is useful in mobile robot localization.V. E XPERIMENTSWe tested the proposed algorithm in indoor environments with a differential-drive mobile platform of Pioneer II (Fig. 9). It carries a laptop computer with a Pentium IV processor clocked at 1.6 GHz and an omnidirectional color camera capturing 30 frames/s with a resolution of 640 × 480 pixels. The artificial landmark was installed at the corners of the indoor environment. The length of equilateral sides of the landmark were set to be 10 cm and the red-blue colored patch was attached for easier detection and tracking. The robot moved at 10 cm/s and the experiment was conducted in real time.Fig. 9 System configurationThe experimental results of the localization are shown in Figs. 10 and 11. A red line denotes the real path. The center of the route is (2, 2) and the radius is 0.9 m. The vehicle starts at the point (2, 1.1) and moves counterclockwise.Fig. 10 Localization result using initial pose estimation methodFig. 11 Result of incremental localization based on EKFThe result using the initial pose calculation method explained in section IV-A is shown in Fig. 10. We can observe that the result is relatively accurate, but the longer the distance between the robot and the landmark, the larger the variance of the estimates. In the experiment, the localization accuracy is mainly dependant on three factors—distance from the landmark, image resolution, and landmark size.Fig. 11 shows the incremental localization result when EKF is adopted. There is a smoothing effect on the direct estimates. The estimation error is generated from systematic errors such as measurement bias, uneven floor, and misalignment between camera and omnidirectional mirror. This error negates the assumption of Gaussian noise.A simulation was conducted to observe the performance of the proposed method where the added noise is Gaussian. The path was the same as for Fig. 10 and noise with D 3.0 standard deviation was applied to the measurements. Simulation results on state estimation error for each entry—φ , ,v v y x —are shown in Fig. 12. The estimate uncertainties become gradually smaller and the estimate errors exist inside the σ3 boundary of the error covariance.Fig. 12 The convergence of the state estimationThe statistical report on estimation error for 200 rotations is shown in TABLE I. The mean positional error is about 7 cm and is reasonable for indoor application.TABLE IE STIMATION E RROR FOR V EHICLE S TATEx(cm)y(cm)Theta(degree) Mean 4.35 4.89 0.99 Maximum 23.83 20.67 4.75VI.C ONCLUSIONWe proposed a simple artificial landmark model and metric localization algorithm for indoor mobile robots.The proposed algorithm based on EKF makes use of a single artificial landmark. The single landmark based algorithm makes the measurement process simple and EKF gradually enhances the estimation accuracy in real time. A geometric method is also proposed in the paper to enable mobile robots to initialize their positions autonomously and to recalculate their pose in case they get lost.The experimental results show the practicality of the method. The experimental results are quite accurate, taking into account the use of only a small single passive landmark.The most deterministic factor in the proposed method is the accuracy of the extracted bearing information for a given image resolution and given setup of artificial landmark.A CKNOWLEDGEMENTThis research has been supported by the Korean Ministry of Science and Technology for National Research Laboratory Program (Grant number M1-0302-00-0064).R EFERENCES[1]V. Ayala, J. B. Hayet, F. Lerasle and M. Devy, “Visual Localization of aMobile Robot in Indoor Environments using Planar Landmarks”, Proceedings of 2000 IEEE International Conference on Intelligent Robots and Systems.[2]M. Mata, J. M. Armingol, A. de la Escalera and M. A. Salichs, “A VisualLandmark Recognition System for Topological Navigation of Mobile Robots”, Proceedings of 2001 IEEE International Conference on Robotics & Automation.[3]Gijeong Jang, Sungho Kim, Wangheon Lee and Inso Kweon, "ColorLandmark Based Self-Localization for Indoor Mobile Robots", Proceedings of 2002 IEEE International Conference on Robotics & Automation.[4]Sung Joon Ahn, W. Rauh and M. Recknagel, “Circular Coded Landmarkfor Optical 3D-Measurement and Robot Vision”, Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. [5]Amy J. Briggs, D. Scharstein, D. Braziunas, C. Dima and P. Wall,“Mobile Robot Navigation Using Self-Similar Landmarks”, Proceedings of 2000 IEEE International Conference on Robotics & Automation.[6]I. Shimshoni, “On Mobile Robot Localization from Landmark Bearings”,IEEE Transactions on Robotics and Automation, vol. 18, no. 6, December 2002.[7]Huoshehg Hu and Dongbing Gu, "Landmark-based Navigation ofIndustrial Mobile Robots", International Journal of Industry Robot, vol.27, no. 6, 2000, pp 458–467.[8]J. M. Buenaposada and L. Baumela, "Real-time Tracking and Estimationof Plane Pose", Proceedings of 2002 IEEE International Conference on Pattern Recognition.[9]G. Simon, A. W. Fitzgibbon and A. Zisserman, "Markerless Trackingusing Planar Structures in the Scene", Proceedings of 2000 IEEE and ACM International Symposium on Augmented Reality.[10]S. Baker and S. K. Nayar, “A Theory of Single-Viewpoint CatadioptricImage Formation”, International Journal of Computer Vision, vol. 35, no.2, pp 1735–196, 1999.[11]Yasushi Yagi, Kousuke Imai, Masahiko Yachida, “Iconic Memory-basedOmnidirectional Route Panorama Navigation”, Proceedings of 2003 IEEE International Conference on Robotics & Automation.[12]J. Canny, “A Computational Approach to Edge Detection”, IEEETransaction on Pattern Analysis & Machine Intelligence, vol. 8, pp 679–698, 1986.[13]Gang Xu and Zengyou Zhang, "Epipolar Geometry in Stereo, Motion andObject Recognition", Kluwer Academic Publishers.。

中英文对照翻译最小化传感级别不确定性联合策略的机械手控制摘要：人形机器人的应用应该要求机器人的行为和举止表现得象人。

下面的决定和控制自己在很大程度上的不确定性并存在于获取信息感觉器官的非结构化动态环境中的软件计算方法人一样能想得到。

在机器人领域，关键问题之一是在感官数据中提取有用的知识，然后对信息以及感觉的不确定性划分为各个层次。

本文提出了一种基于广义融合杂交分类（人工神经网络的力量，论坛渔业局）已制定和申请验证的生成合成数据观测模型，以及从实际硬件机器人。

选择这个融合，主要的目标是根据内部（联合传感器）和外部（ Vision 摄像头）感觉信息最大限度地减少不确定性机器人操纵的任务。

目前已被广泛有效的一种方法论就是研究专门配置5个自由度的实验室机器人和模型模拟视觉控制的机械手。

在最近调查的主要不确定性的处理方法包括加权参数选择（几何融合），并指出经过训练在标准操纵机器人控制器的设计的神经网络是无法使用的。

这些方法在混合配置，大大减少了更快和更精确不同级别的机械手控制的不确定性，这中方法已经通过了严格的模拟仿真和试验。

关键词：传感器融合，频分双工，游离脂肪酸，人工神经网络，软计算，机械手，可重复性，准确性，协方差矩阵，不确定性，不确定性椭球。

1 引言各种各样的机器人的应用（工业，军事，科学，医药，社会福利，家庭和娱乐）已涌现了越来越多产品，它们操作范围大并呢那个在非结构化环境中运行 [ 3,12,15]。

在大多数情况下，如何认识环境正在发生变化且每个瞬间最优控制机器人的动作是至关重要的。

移动机器人也基本上都有定位和操作非常大的非结构化的动态环境和处理重大的不确定性的能力[ 1,9,19 ]。

每当机器人操作在随意性自然环境时，在给定的工作将做完的条件下总是存在着某种程度的不确定性。

这些条件可能，有时不同当给定的操作正在执行的时候。

导致这种不确定性的主要的原因是来自机器人的运动参数和各种确定任务信息的差异所引起的。

同步定位与地图构建（来自维基百科，自由的百科全书）同步定位与地图构建（SLAM或Simultaneous localization and mapping）是一种概念：希望机器人从未知环境的未知地点出发，在运动过程中通过重复观测到的地图特征（比如，墙角，柱子等）定位自身位置和姿态，再根据自身位置增量式的构建地图，从而达到同时定位和地图构建的目的。

目录1 操作性定义2 技术上的问题2.1 地图构建2.2 传感2.3 定位2.4 建模3 相关文献4 参见5 参考资料6 外部链接1 操作性定义这里说的地图，是用来在环境中定位，以及描述当前环境以便于规划航线的一个概念；它通过记录以某种形式的感知获取的信息，用以和当前的感知结果相比较，以支撑对现实定位的评估。

在定位评估方面，地图提供的帮助程度，与感知的精度和质量成反相关。

地图通常反映了它被描绘出来的时刻的环境状态，所以它并不一定反映它被使用的时刻的环境状态。

在误差和噪音条件下，定位和地图构建技术上的复杂度不支持两者同时获得连续的解。

即时定位与地图构建（SLAM）是这样一个概念：把两方面的进程都捆绑在一个循环之中，以此支持双方在各自进程中都求得连续解；不同进程中相互迭代的反馈对双方的连续解有改进作用。

地图构建，是研究如何把从一系列传感器收集到的信息，集成到一个一致性的模型上的问题。

它可以被描述为第一核心问题：这个世界长什么样？地图构建的核心部分是环境的表达方式以及传感器数据的解释。

与之相比，定位，是在地图上估测机器人的坐标和姿势形态的问题；换而言之，机器人需要回答这里的第二核心问题，我在哪？典型的解包含以下两个方面：追踪——通常机器人的初始位置已知；全局定位——通常只给出很少，甚至不给出有关于起始位置环境特征的先验信息。

所以，同步定位与地图构建（SLAM）被定义为以下问题：在建立新地图模型或者改进已知地图的同时，在该地图模型上定位机器人。

实际上，这两个核心问题如果分开解决，将毫无意义；必须同时求解。

移动机器人自主导航矩阵二维码辅助定位研究张安峰;吴涛;杨丛丛【摘要】移动机器人自主导航过程中,为机器人提供精确地位置信息十分重要,只有机器人有了精确地位置信息才能准确地导航到目标点.由于单纯的惯性和里程测量系统的相对定位方式都不能消除长时间的累计定位误差,因此需要一种绝对位置信息加以辅助修正累计误差.二维码是一种很好的可以存储绝对位置信息的方式,且信息获取简单易用,本文提出了一种将矩阵二维码作为绝对位置标签辅助修正里程和惯性测量系统导航过程中产生的累计误差的导航定位方法.通过实验对比验证了该方法的有效性.【期刊名称】《软件》【年(卷),期】2019(040)006【总页数】5页(P180-184)【关键词】移动机器人;二维码;自主导航;定位【作者】张安峰;吴涛;杨丛丛【作者单位】昆明理工大学机电工程学院,云南昆明 650500;昆明理工大学机电工程学院,云南昆明 650500;昆明理工大学机电工程学院,云南昆明 650500【正文语种】中文【中图分类】TP242.60 引言现代科技技术发展迅速，各类传感器产品技术稳定且精度不断提高，致使一些依赖各类传感器的新兴技术发展迅速。

其中移动机器人就是一个典型的代表，近几年，成为了国内外及各大高校的研究热点。

其中主要包括 SLAM(同时定位与建图)、自主导航、定位等[1]。

不同环境机器人的定位方法分为相对定位和绝对定位，相对定位主要通过里程测量和惯性导航技术[2]，主要传感器为惯性测量（IMU）传感器和电机编码器等。

室外机器人的绝对定位主要通过GPS辅助机器人定位[3]，而对于室内机器人，则需要借助绝对位置传感器如 RFID、RSSI、UWB、WIFI、激光、视觉等技术来辅助机器人获得绝对位姿,这类传感器不仅仅应用于机器人[4-6]，在其他室内定位方面也同样适用[7-9]。

现如今，标签式辅助定位非常流行，因为相比于前面提到的室内绝对定位方式，标签式辅助定位其信息获取较为简单，且成本低。

基于里程计和PTZ视觉的移动机器人自定位牛国臣;徐萍;冯琦【期刊名称】《计算机应用》【年(卷),期】2011(31)10【摘要】针对机器人长距离运行时里程计定位存在累积误差问题,提出一种基于里程计和PTZ视觉的移动机器人自定位算法.提出了中断式S形搜索策略的概念,设计了基于有限自动机的视觉定位方法；分析了里程计和视觉定位误差来源,分别建立了其定位信度模型；并基于该模型建立里程计和PTZ视觉定位的框架.针对视觉定位及里程计视觉复合定位分别进行了实验,结果验证了该方法的有效性和实用性.%To reduce the influence of cumulative odometer localization error, an autonomous localization approach for mobile robot based on odometer and Pan-Tilt-Zoom (PTZ) vision was presented. Interrupt S-shaped searching strategy was put forward, as well as a vision localization approach based on finite automaton. The position credibility models of odometer and PTZ vision were established according to their positioning error sources. A localization framework combining odometer and PTZ vision based on real-time position credibility was designed. An application experiment of vision localization and localization using odometer and PTZ vision was carried out. The validity and practicability of the method are confirmed.【总页数】4页(P2821-2824)【作者】牛国臣;徐萍;冯琦【作者单位】中国民航大学机器人研究所,天津300300;北京航空航天大学机器人研究所,北京100191;中国民航大学机器人研究所,天津300300;中国民航大学机器人研究所,天津300300【正文语种】中文【中图分类】TP242.6【相关文献】1.基于人工路标和立体视觉的移动机器人自定位 [J], 刘振宇;姜楠;张令涛2.基于几何-拓扑广域三维地图和全向视觉的移动机器人自定位 [J], 王珂;王伟;庄严;孙传昱3.基于单目视觉里程计的移动机器人自定位方法 [J], 雷越;邓斌;何沛恒;徐新;左荣4.基于改进视觉里程计算法的移动机器人定位 [J], 任平洋;李文锋5.基于视觉的移动机器人自定位问题 [J], 卢惠民;张辉;郑志强因版权原因，仅展示原文概要，查看原文内容请购买。