移动机器人扩展卡尔曼滤波定位与传感器误差建模

针对里程计在定位过程中存在累积误差的问题,建立了一种通用的移动机器人里程计误差模型,对里程计误差进行实时反馈补偿.在利用激光雷达进行环境特征提取过程中,根据激光雷达原始数据存在的误差,建立了激光雷达的观测误差模型,并根据环境特征和机器人的相对位置关系,建立了移动机器人观测模型.最后,结合里程计和激光雷达误差模型,利用扩展卡尔曼滤波(EKF)实现了基于环境特征跟踪的移动机器人定位.实验结果验证了里程计和激光雷达误差模型的引入,在增加较短定位时间的情况下,可以有效地提高移动机器人的定位精度.     

轮式移动机器人里程计系统误差校核*

里程计使用编码器为轮式移动机器人提供基本的位姿估计,在运行过程中里程计存在严重的误差累计,通过校核系统参数可以减小系统误差,UMBmark方法是轮式移动机器人广泛使用的系统误差校核方法。针对UMBmark方法存在的不足,提出一种改进的系统误差校核新方法：综合考虑三种主要系统误差来源产生的误差对移动机器人直线运动和定点旋转运动造成的影响,同时采用正方形回路终点的方向误差代替传统UMBmark方法中的位置误差来校核系统参数。实验结果表明提出的方法能够有效校核系统参数,提高移动机器人的定位精度。     

移动机器人里程计系统误差及激光雷达安装误差在线标定

为了降低传感器系统误差所带来的影响,首先建立了差速移动机器人里程计系统误差及激光雷达安装误差数学模型.然后,基于拓展卡尔曼滤波算法,提出了一种里程计系统误差及激光雷达安装误差迭代标定方法,该方法能够在定位的同时对2组误差进行实时标定.通过仿真对该方法进行验证,误差估计有效地收敛到误差真值.实物实验中,误差估计能有效收敛,标定后的航迹推算误差大幅度降低.     

未知环境下的移动机器人定位及实时避障

针对未知环境下移动机器人实时动态避障及定位问题,考虑里程计定位的无界累加误差和动态障碍物环境下实时障碍躲避需要,提出了一种可行的避障定位的策略.该策略融合了机器人内部传感器、里程计、电子罗盘和激光测距仪的同步和异步信息,合理地解决了常规定位过程中的方向迷失问题,对于静态和动态障碍物都能很好地实时躲避,具有很强的抗干扰性和较高的定位精度.实验证明了该方法的有效性和实用性.     

未知环境下的移动机器人定位及实时避障

针对未知环境下移动机器人实时动态避障及定位问题，考虑里程计定位的无界累加误差和动态障碍物环境下实时障碍躲避需要，提出了一种可行的避障定位的策略。该策略融合了机器人内部传感器、里程计、电子罗盘和激光测距仪的同步和异步信息，合理地解决了常规定位过程中的方向迷失问题，对于静态和动态障碍物都能很好地实时躲避，具有很强的抗干扰性和较高的定位精度。实验证明了该方法的有效性和实用性.     

基于多传感器融合的移动机器人定位算法研究

针对移动机器人在室外环境下全局位姿定位精度低、定位耗时长的问题,提出一种基于多传感器融合的机器人定位算法。首先构建移动机器人的运动模型,并选用里程计、惯性测量单元IMU和激光雷达作为移动机器人的基础传感器;然后采用自适应蒙特卡罗定位算法对传感器融合位姿进行位姿误差计算,获取移动机器人初始位姿;最后进行激光点云匹配,获取全局地图,并利用基于全局正态分布地图的NDT算法进行初始位姿修正,最终实现全局位姿校正和高精度定位。结果表明,基于多传感器融合的移动机器人定位误差控制在0.04 m范围内,定位时长均值为0.045 s,定位误差较小,定位损耗时间较少。由此说明,本定位算法可提升移动机器人的定位精度和定位效率,可实现移动机器人全局位姿快速、精确定位,提出的定位算法具备一定的有效性。     

基于多传感器融合的移动机器人定位

针对移动机器人定位系统中单一传感器定位精度低与环境地图的重要性问题, 提出了一种基于多传感器融合的移动机器人定位方法. 首先, 在未知环境下, 分别利用单一里程计, 扩展卡尔曼滤波(extended Kalman filter,EKF)算法融合里程计、惯性测量单元(inertial measurement unit, ...     

基于捷联惯导和里程计的井下机器人定位方法研究

针对现有煤矿井下移动机器人定位方法存在定位难、精度低的问题,提出了一种基于捷联惯导和里程计的井下机器人定位方法。该方法利用卡尔曼滤波对捷联惯导进行初始对准,以此确定定位的初始坐标,得到初始姿态转换矩阵;利用捷联惯导独立完成机器人位置解算,同时利用里程计输出的速度信息与捷联惯导输出的实时姿态转换矩阵进行航位推算解算,再次得到机器人的位置信息;为了减少累积误差对捷联惯导的影响,使用里程计和捷联惯导构成航位推算系统,采用Sage-Husa自适应滤波设计组合定位算法,选择误差作为系统状态,经过滤波计算和校正,可获得机器人的精确位置信息。实验结果表明,该方法可实现机器人实时定位,有效减少捷联惯导累积误差的影响;定位精度较高,机器人在Y向运动4.3m,Z向运动0.25m后,Y向定位误差为0.25m,Z向定位误差为0.005m。     

基于改进的3维ICP匹配的单目视觉里程计

针对目前流行的单目视觉里程计当移动机器人做“近似纯旋转运动”时鲁棒性不强的问题,从理论上分析了其定位鲁棒性不高的原因,提出了一种基于改进的3维迭代最近点（ICP）匹配的单目视觉里程计算法．该算法首先初始化图像的边特征点对应的深度值,之后利用改进的3维ICP算法迭代求解2帧图像之间对应的3维坐标点集的6维位姿,最后结合边特征的几何约束关系利用扩展卡尔曼深度滤波器更新深度值．改进的ICP算法利用反深度不确定度加权、边特征梯度搜索与匹配等方法,提高了传统ICP算法迭代求解的实时性和准确性．并且将轮子里程计数据作为迭代初始值,能够进一步提高定位算法的精度和针对“近似纯旋转运动”问题的鲁棒性．本文采用3个公开数据集进行算法验证,该算法在不损失定位精度的前提下,能够有效提高针对近似纯旋转运动、大场景下的鲁棒性．单目移动机器人利用本文算法可在一定程度上校正里程计漂移的问题．     

新的室内移动机器人自定位方法

针对现有室内移动机器人自定位方法中存在的定位精度不高,随时间积累定位误差增大,复杂室内环境下信号存在多径效应和非视距效应等问题,提出了一种基于蒙特卡罗定位(MCL)的新的移动机器人自定位方法。首先,通过分析基于无线射频识别(RFID)技术的移动机器人自定位系统,建立机器人运动模型;然后,通过分析基于接收信号强度指示(RSSI)的移动机器人自定位系统,提出机器人移动过程的观测模型;最后,针对粒子滤波定位执行效率不高的问题,提出粒子剔除策略和依据粒子方位赋予粒子权值策略,提高系统的定位精度和执行效率。仿真实验表明,机器人在移动过程中的自定位误差在X轴和Y轴方向上为3 cm,传统定位算法误差为6cm,新算法定位精度提高近1倍,且算法具有很好的鲁棒性。     

多传感器信息融合在移动机器人定位中的应用

机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。     

融合测程法与视觉信息的足球机器人自定位方法

针对足球机器人自定位问题,提出一种融合测程法与视觉信息的定位方法。方法综合考虑两种信息的特点,有效实现优势互补:一方面,针对视觉定位易出现的歧义,利用测程法获得的定位结果予以有效消解;另一方面,随着运动,测程法定位易出现误差的累积,利用消歧后的视觉定位结果加以动态修正。最后,在Webots模拟平台上进行的机器人球场定位实验表明文中方法的有效性。     

Mobile robot localization with gyroscope and constrained Kalman filter

The odometry information used in mobile robot localization can contain a significant number of errors when robot experiences slippage. To offset the presence of these errors, the use of a low-cost gyroscope in conjunction with Kalman filtering methods has been considered by many researchers. However, results from conventional Kalman filtering methods that use a gyroscope with odometry can unfeasible because the parameters are estimated regardless of the physical constraints of the robot. In this paper, a novel constrained Kalman filtering method is proposed that estimates the parameters under the physical constraints using a general constrained optimization technique. The state observability is improved by additional state variables and the accuracy is also improved through the use of a nonapproximated Kalman filter design. Experimental results show that the proposed method effectively offsets the localization error while yielding feasible parameter estimation.     

Application of Localization Based on the DC Magnetic Field that Occurs in the Environment on Wheel-Type Mobile Agricultural Robots

A wheel-type mobile robot is simply able to localize with odometry. However, for mobile agricultural robots, it is necessary to consider that the environment is uneven terrain. Therefore, odometry is unreliable and it is necessary to augment the odometry by measuring the position of the robot relative to known objects in the environments. This paper describes the application of localization based on the DC magnetic field that occurs in the environment on mobile agricultural robots. In this research, a magnetic sensor is applied to scan the DC magnetic field to build a magnetic database. The robot localizes by matching magnetic sensor readings against the magnetic database. The experimental results indicate that the robot is able to localize accurately with the proposed method and the cumulative error can be eliminated by applying the localization results to compensate for the odometry.     

Relative localization using path odometry information

All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder’s information is integrated to derive the robot’s position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct. Nakju Lett Doh received his BS, his MS, and his Ph.D. degree in Mechanical Engineering from Pohang University of Science and Technology (POSTECH), KOREA, in 1998, 2000, and 2005, respectively. Since then, he is a senior researcher in Intellgient Robot Reserarch Division, Electronics and Telecommunications Research Institute (ETRI), KOREA. He received the glod prize in Intelligent Robot Contest hosted by Northern KyoungSang Province at 2000 and the gold prize in Humantech Thesis Competition hosted by Samsung Electronics at 2005. In 2003, he got the best student paper award in IEEE International Conference on Robotics and Automation held in Taiwan. His research interests are the localization and navigation of mobile robots and ubiquitous robotic space for intelligent robot navigation. Howie Choset is an Associate Professor of Robotics at Carnegie Mellon University where he conducts research in motion planning and design of serpentine mechanisms, coverage path planning for de-mining and painting, mobile robot sensor based exploration of unknown spaces, and education with robotics. In 1997, the National Science Foundation awarded Choset its Career Award to develop motion planning strategies for arbitrarily shaped objects. In 1999, the Office of Naval Research started supporting Choset through its Young Investigator Program to develop strategies to search for land and sea mines. Recently, the MIT Technology Review elected Choset as one of its top 100 innovators in the world under 35. Choset directs the Undergraduate Robotics Minor at Carnegie Mellon and teaches an overview course on Robotics which uses series of custom developed Lego Labs to complement the course work. Professor Choset’s students have won best paper awards at the RIA in 1999 and ICRA in 2003. Finally, Choset is a member of an urban search and rescue response team using robots with the Center for Robot Assisted Search and Rescue. Now, he is active in extending the mechanism design and path planning work to medical mechatronics. Wan Kyun Chung received his BS degree in Mechanical Design from Seoul National University in 1981, his MS degree in Mechanical Engineering from KAIST in 1983, and his Ph.D. in Production Engineering from KAIST in 1987. He is Professor in the school of Mechanical Engineering, POSTECH (he joined the faculty in 1987). In 1988, he was a visiting professor at the Robotics Institute of Carnegie-Mellon University. In 1995 he was a visiting scholar at the university of California, Berkeley. His research interests include the localization and navigation for mobile robots, underwater robots and development of robust controller for precision motion control. He is a director of National Research Laboratory for Intelligent Mobile Robot Navigation. He is serving as an Associate Editor for IEEE Tr. on Robotics, international editorial board for Advanced Robotics.     

High resolution relative localisation using two cameras

Wheel odometry is a common method for high resolution relative localisation. However, wheel odometry relies on the integrity and accuracy of a kinematic model. In this paper, a new method for relative localisation, ‘visiodometry’, which does not rely on a kinematic model, is proposed. The system consists of two ground-facing cameras mounted on either side of the robot. From the sequence of images acquired, the relative change in pose of the robot is estimated using a phase correlation based method. Results on a plain coloured carpeted surface, show that the method provides a truly odometric type sensor data input similar in modality and resolution to wheel odometry. A method to calibrate the visiodometry system using a 1D object is also presented.     

Gyro-based odometry associated with steering characteristics for wheeled mobile robot in rough terrain

Mobile robot used for planetary exploration performs several scientific missions over long distance travel and needs to have a high degree of autonomous mobility system because the communication delay from the Earth impedes its direct teleoperation. Localization of a mobile robot is of particular importance on the autonomous mobility. Classical localization methods such as wheel/visual odometry have been widely investigated and demonstrated, but they possess a well-known trade-off between computational cost and localization accuracy. This paper proposes an accurate gyro-based odometry method for a wheeled mobile robot in rough terrain. The robot in rough terrain is often subject to large wheel slip or vehicle sideslip related with its steering maneuver, and those slips degrade the localization accuracy. The basic approach of the proposed method is to exploit odometry data for the robot distance traveled as well as gyroscope data for the robot heading calculation; however each data-set is weighted in accordance with steering characteristics of a robot in rough terrain. The usefulness of the proposed method is examined through field experiments using a wheeled mobile robot testbed in Martian analog site. The experimental result confirms that the proposed method accurately estimates the robot trajectory.     

Motion planning algorithms of an omni-directional mobile robot with active caster wheels

This work deals with motion planning algorithms of an omni-directional mobile robot with active caster wheels. A typical problem occurred in the motion control of such omni-directional mobile robot, which has been identified through experimental experiences, is skidding of the mobile wheel. It sometimes results in uncertain rotation of the steering wheel. To cope with this problem, a motion planning algorithm which resolves the skidding problem and uncertain motions of the steering wheel is mainly investigated. For navigation of the mobile robot, the posture of the omni-directional mobile robot is initially calculated using the odometry information. Then, the accuracy of the mobile robot’s odometry is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through simulations and experimentation, the feasibility of proposed algorithms was verified.     

嵌入式移动机器人红外路标定位模块研究

针对室内环境移动机器人的自定位问题,提出一种嵌入式移动机器人红外路标定位模块。采用基于单应矩阵的初始标定算法和陒机初始标定方法,补偿由于实际使用中的安装误差所引起的定位偏差。实验结果表明,该模块易于嵌入式系统实现,定位模块位置精度可达厘米级别,角度定位精度雓于6°。