基于集员估计的室内移动机器人多传感器融合定位

本文针对室内移动机器人的长距离实时鲁棒定位问题进行了研究,考虑到单一定位手段存在的不足,以二维扫描激光和里程计作为主要的定位设备,采用多传感器数据融合技术实现了移动机器人的精确定位.论文首先通过引入基于点-直线特征匹配的改进迭代最近邻(iterative closest point,ICP)扫描匹配方法对激光采集的环境点云信息进行迭代匹配以得到相对位姿变换估计,并推导了其估计不确定性的保守包络矩阵形式,然后通过建立定位过程和观测模型,引入扩展非线性集员滤波器作为多传感器融合方法,利用扫描匹配结果校正由里程计滑移带来的定位误差,并获取定位自身的不确定性边界估计.实验结果表明了本文所提出的室内定位方法的精度、实时性和鲁棒性.     

基于非线性优化的激光雷达在线标定算法

为解决移动机器人定位过程中激光雷达位姿标定问题,保证机器人在作业过程中保持外参的精确,分析里程计与激光雷达的外参标定问题,提出基于非线性优化方式的激光雷达位姿标定算法.通过移动机器人里程计与激光帧间匹配之间的关系建立非线性优化的数学模型,根据优化方式求取较为精确的激光雷达位姿信息.该方法灵活性强,可适应不同场景进行标定,在实际机器人进行算法验证,验证结果表明,该算法有效可行,能够较为精确得到激光雷达的位姿.     

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.     

一种有效的移动机器人里程计误差建模方法

移动机器人里程计误差建模是研究移动机器人定位问题的基础. 现有的移动机器人里程计误差建模方法多数针对某一种驱动类型移动机器人设计, 运动过程中缺乏对里程计累计误差的实时反馈补偿, 经过长距离运动过程定位精度大幅度降低. 因此本文针对同步驱动和差动驱动轮式移动机器人平台提出了一种通用的里程计误差建模方法. 在假设机器人运动路径近似弧线基础上, 依据里程计误差传播规律推导了非系统误差、系统误差与里程计过程输入之间的近似函数关系, 进而提出一种具有闭环误差实时反馈补偿功能的移动机器人定位算法, 对定位过程中产生的里程计累计误差给予实时反馈补偿. 实验表明新算法有效地减少了里程计累计误差, 提高了定位精度.     

基于里程计和PTZ视觉的移动机器人自定位

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。     

面向地下空间探测的移动机器人定位与感知方法

提出了一种面向地下空间探测的移动机器人定位与感知方法。首先,针对地下空间的结构退化问题,构建了基于因子图的激光雷达／里程计／惯性测量单元紧耦合融合框架;推导了高精度惯性测量单元／里程计的预积分模型,利用因子图算法实现对移动机器人运动状态及传感器参数的同步估计。同时,提出了基于激光雷达／红外相机融合的目标识别方法,能够对弱光照环境下的多种目标进行识别与相对定位。试验结果表明,在结构退化环境中,本文方法能够将移动机器人的定位精度提升50%以上,并对弱光照环境中的目标实现厘米级的相对定位精度。     

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。     

移动机器人多传感器定位融合的SLAM建图

移动机器人仅依赖于轮式里程计进行定位时,常易积累误差并受到环境干扰,导致其建图效果并不理想。二维激光雷达在移动机器人中的运用也显示出明显的局限性,无法全面准确地获取空间信息及定位。为了提升建图和定位的准确性以及效率,机器人需要利用更多类型和更先进的传感器技术。引入视觉惯性里程计（VIO）到SLAM方案中,能帮助机器人获取更精准的姿态信息,从而提升自主定位和地图构建的能力。此外,多个传感器信息的融合可以解决单一传感器带来的误差和不确定性问题,进一步提升定位和运动的精度。     

基于激光雷达与双目视觉的移动机器人SLAM研究

为解决移动机器人在环境未知条件下,利用单一传感器自主导航时不能及时定位、构建地图不精确的问题,提出采用一种改进RBPF算法,在计算提议分布时将移动机器人的观测数据(视觉信息与激光雷达信息)和里程计信息融合;针对一般视觉图像特征点提取算法较慢的问题,采用基于ORB算法对视觉图像进行处理以加快视觉图像处理速度的方法;最后通过在安装有开源机器人操作系统(ROS)的履带式移动机器人进行实验,验证了采用该方法可构建可靠性更高、更精确的2D栅格图,提高了移动机器人SLAM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。     

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.     

基于Kinect的三维视觉里程计的设计

针对移动服务机器人在未知环境下三维路径估计的问题,设计了一种基于Kinect的实时估计机器人运动轨迹的方法。该方法采用Kinect获取机器人运动过程中连续帧的彩色和深度信息,首先,提取并匹配目标帧和参考帧的SURF的特征点;然后,结合深度信息利用经典P3P问题的方法及改进的随机采样一致性(RANSAC)算法计算机器人的初始6自由度(DOF)位姿;最后,通过非线性最小二乘算法最小化初始位姿内点的双向投影误差来提高位姿精度,进而得到机器人的运动轨迹。同时对比了不同特征点及描述符结合下的里程计精度。实验结果表明,所提方法能够将里程计误差降低到3.1%,且能够满足实时要求,可为机器人同时定位与地图创建提供重要的先验信息。     

基于多传感信息融合的语义词袋SLAM优化算法

针对室外大范围场景移动机器人建图中,激光雷达里程计位姿计算不准确导致SLAM (simultaneous localization and mapping)算法精度下降的问题,提出一种基于多传感信息融合的SLAM语义词袋优化算法MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags)。采用视觉惯性系统引入激光雷达原始观测数据,并通过滑动窗口实现了IMU (inertia measurement unit)量测、视觉特征和激光点云特征的多源数据联合非线性优化;最后算法利用视觉与激光雷达的语义词袋互补特性进行闭环优化,进一步提升了多传感器融合SLAM系统的全局定位和建图精度。实验结果显示,相比于传统的紧耦合双目视觉惯性里程计和激光雷达里程计定位,MSW-SLAM算法能够有效探测轨迹中的闭环信息,并实现高精度的全局位姿图优化,闭环检测后的点云地图具有良好的分辨率和全局一致性。     

基于多传感器融合的机器人编队ADRC控制

针对移动机器人编队问题,设计了一种基于多传感器信息融合和自抗扰控制器的编队控制系统。首先,为提高机器人的定位精度,采用卡尔曼滤波算法对激光数据和里程计数据进行融合,以更加精确的获得移动机器人的坐标信息,并建立主从机器人轨迹跟踪误差模型。进而设计了自抗扰控制器,完成扩张状态观测器以及控制规律的设计,实现移动机器人的跟踪编队控制。最后,设计了编队控制实验平台,并在该平台上验证了所提出方法的有效性和优越性。     

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.     

高精度激光测距下的机器人自主避障控制

在机器人自主避障过程中,由于传感器数据的误差会降低机器人感知和决策的准确性,从而影响机器人自主避障能力。为此,提出高精度激光测距下的机器人自主避障控制方法。通过设计机器人体系结构,建立机器人运动学模型,为机器人避障控制提供依据。采用高精度激光测距技术,构建机器人移动场地地形。通过自适应阈值方法,完成机器人的自主避障控制。实验结果表明,所提方法的机器人自主避障控制效果好,且障碍物位置测试值与实际位置值的误差保持在0.5m以内,具有较高的避障控制精确度。     

WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法

针对移动机器人的定位问题,提出一种面向无线传感器网络WSNs( Wireless Sensor Networks)环境下,结合高斯混合容积卡尔曼滤波( GM￣CKF)优化的定位算法。将WSNs对移动机器人的观测、机器人自身对环境特征的观测以及机器人自身运动控制量进行数据融合,并利用带有门限判别和选择性高斯分割的GM￣CKF算法,对机器人的预估位置实施预测修正,降低计算求解的空间维数,提高定位精度。仿真实验结果表明,所提出的方法比传统机器人自定位法定位精度有所提高,算法精度较标准的CKF算法提高了39.11％,比EKF算法提高了65.81％。     

具有非完整约束移动机器人的超宽带-惯导-里程计融合定位与能观测性分析

为解决现有超宽带-惯导组合定位系统在轮式移动机器人的定位精度低、依赖高精度IMU等问题,提出了一种采用误差状态卡尔曼滤波融合超宽带-惯导-里程计的定位算法,利用里程计的线速度测量和由非完整约束隐含的伪测量,提高了移动机器人的位置和姿态估计精度. 同时,对于由多传感器测量模型组成的非线性系统,通过基于李导数的能观性秩条件分析方法对该系统的能观测性进行了详细的理论分析与数学证明,得到了系统局部弱可观的条件,从而确定了系统状态可以被无偏估计所需要的测量输出以及控制输入. 仿真结果表明,在满足能观测性条件时,本文提出的方法能够有效地获得移动机器人较准确的六自由度位姿,且相比传统方法显著提升了定位精度.