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。     

基于等效陀螺的捷联惯导快速两位置对准仿真

研究优化惯导系统的可观性能,提高捷联惯导两位置对准的速度,有助于提高导航的准确性和快速性,将水平速度误差和等效陀螺误差作为系统观测量,提出采用静基座下快速两位置对准的新方法.在常规对准方法的基础上,建立了引入陀螺信息的观测方程,用奇异值分解方法分析了新系统可观测性,推出了第一位置可观测量的最优估计及估计误差.根据状态快速收敛的特性,提出采用PCWS方法,对加速度计和陀螺进行了有效的标定,并进行了仿真,结果证明方法有效提高加快了两位置对准的速度,能充分利用陀螺信息,有效提高了惯导系统的可观测性,缩短对准时间,具有重要的参考价值.     

基于OMAPL138与FPGA的惯性姿态测量系统设计与实现

为了满足高性能、低成本及多接口的惯导使用需求,设计一种基于OMAPL138+FPGA的大存储空间惯性姿态测量系统;系统设计充分利用OMAPL138的异构双核结构,结合每种处理器应用特点,进行任务划分并构建硬件平台;设计了丰富的外围接口,通过选择接入GPS、北斗或里程计,能够实现多种组合导航方式;根据使用环境提出惯导与里程计组合导航方案和相应软件流程,并进行了姿态精度测量及导航定位精度试验;姿态测量精度优于0.5密位,纯惯性导航定位精度为0.3‰ D (CEP),组合导航的定位精度为0.14‰,试验结果表明,系统稳定可靠,硬件平台满足惯导计算机设计需求。     

基于捷联惯导/里程计的车载高精度定位定向方法研究

研究了一种基于捷联惯导系统和里程计进行车载高精度定位定向的方法;采用里程计与捷联惯导中的陀螺仪构成航位推算系统,建立了航位推算系统的误差模型;为了增强航位推算姿态误差的估计效果,将载车姿态与位置信息一起作为量测,构建组合定位定向的量测方程;采用Sage-Husa自适应滤波设计捷联惯导/里程计组合定位定向算法,以增强算法对外界环境和载车机动的鲁棒性;仿真结果表明,基于捷联惯导/里程计的车载定位定向方法能够达到±20.4m(3σ)的定位精度,航向精度达到±3.1′(3σ),水平姿态精度达到±0.6′(3σ)。     

改进量测的车载捷联惯导/里程计组合导航算法

针对车载SINS/Odometer速度量测和位置量测组合导航算法的潜在缺陷,提出了改进量测算法.在分析里程计输出误差模型的基础上,将里程计标度因数误差和惯导系统安装偏差均列入状态变量,并将1 s内惯导位置增量和航位推算位置增量之差作为量测值进行组合导航.车载试验使用精度为0.02°/h的激光陀螺捷联惯导系统,采用该算法后行驶54 km,定位精度可达行驶里程的0.08％.离线分析进一步证实,改进算法可以快速估计出各项传感器误差、安装偏差和初始对准误差,从而保证系统的定位和定向精度.     

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.     

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。     

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.     

基于UKF的惯导/被动导引头组合位置修正方法

在导弹末制导阶段,结合自适应UKF滤波方法和直接滤波模型实现惯导/被动导引头组合导航,以提高惯导系统定位精度.首先采用惯导参数和导引头视线信息建立非线性模型,然后结合模型特点对UKF滤波流程进行简化,并针对系统状态变化较快和量测噪声不稳定的特点给出改进的自适应UKF滤波方法,能实现视线信息辅助修正惯导系统位置误差.仿真结果表明,该滤波方法具有较高的估计精度和较快的收敛速度,7s时惯导/被动导引头组合定位误差不高于30m.     

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。     

移动机器人超宽带与视觉惯性里程计组合的室内定位算法

对于移动机器人在室内环境的定位任务,新兴的基于视觉惯性里程计（VIO）的辅助定位技术受光线条件限制大,无法在黑暗环境中工作,且超宽带（UWB）定位易受非视距（NLOS）误差影响。针对以上问题,提出一种UWB与VIO组合的室内移动机器人定位算法。首先,采用立体视觉多状态约束下的Kalman滤波器（S-MSCKF）算法/双边双向测距（DS-TWR）算法和三边定位法,分别得到VIO输出的位置信息/UWB解算的定位信息;然后,建立位置测量系统的运动方程与观测方程;最后,通过误差状态扩展卡尔曼滤波（ES-EKF）算法来进行数据融合,得到机器人的最优位置估计。使用搭建的移动定位平台在不同的室内环境下对组合定位方算法进行验证。实验结果表明在有障碍物的室内环境下,与单一UWB定位方法相比,所提算法的总体定位的最大误差减小了约4.4%,均方误差减小了约6.3%;与VIO定位方法相比,所提算法的总体定位的最大误差减小了约31.5%,均方误差减小了约60.3%。可见所提算法可为室内环境下的移动机器人提供实时、精确且鲁棒的定位结果。     

Self‐alignment and calibration of a MEMS IMU: Improving observability and estimability by rotary motions of IMU

The stationary self‐alignment and calibration (SSAC) for a low‐cost MEMS IMU is quite challenging due to the poor observability of an inertial system under static condition and the significant sensor errors of MEMS inertial sensors. This research proposes to employ IMU rotations to improve the system observability and estimability regarding the SSAC of a low‐cost MEMS IMU. IMU rotations about the X, Y, and Z axes are employed in this paper. The analytic estimation algorithm for each error state is derived and the observability of the system with IMU rotation is analyzed. As the observability analysis will not provide clues about how well an error state can be estimated, the estimability analysis is also conducted based on the eigenvalues and eigenvectors from the covariance matrix in the Kalman filter. Tests are conducted with a tri‐axial turntable to verify the improvements on system observability and estimability brought by IMU rotations. Of both theoretical analysis and results indicated with proper IMU rotations, only azimuth error still remains unobservable, and the IMU rotation also significantly improves the estimability of all error states, including the unobservable azimuth.     

基于UKF的UWB和GPS融合定位算法

智能汽车的发展对高精度定位需求日益显现. 针对汽车在城市建筑群、立交桥等特定环境下, 可见GPS卫星数量下降、车载GPS和惯性测量单元(inertial measurement unit, IMU)组合定位系统中IMU产生积累误差导致不能精确定位问题, 本文提出一种基于无迹卡尔曼滤波(unscented Kalman ...     

Kinematic calibration for collaborative robots on a mobile platform using motion capture system

For modern robotic applications that go beyond the typical industrial environment, absolute accuracy is one of the key properties that make this possible. There are several approaches in the literature to improve robot accuracy for a typical industrial robot mounted on a fixed frame. In contrast, there is no method to improve robot accuracy when the robot is mounted on a mobile base, which is typical for collaborative robots. Therefore, in this work, we proposed and analyzed two approaches to improve the absolute accuracy of the robot mounted on a mobile platform using an optical measurement system. The first approach is based on geometric operations used to calculate the rotation axes of each joint. This approach identifies all rotational axes, which allows the calculation of the Denavit–Hartenberg (DH) parameters and thus the complete kinematic model, including the position and orientation errors of the robot end-effector and the robot base. The second approach to parameter estimation is based on optimization using a set of joint positions and end-effector poses to find the optimal DH parameters. Since the robot is mounted on a mobile base that is not fixed, an optical measurement system was used to dynamically and simultaneously measure the position of the robot base and the end-effector. The performance of the two proposed methods was analyzed and validated on a 7-DoF Franka Emika Panda robot mounted on a mobile platform PAL Tiago-base. The results show a significant improvement in absolute accuracy for both proposed approaches. By using the proposed approach with the optical measurement system, we can easily automate the estimation of robot kinematic parameters with the aim of improving absolute accuracy, especially in applications that require high positioning accuracy.     

State estimation of mobile robot using GPS signal that includes reflected and diffracted waves

We propose a method to improve the state estimation accuracy of mobile robots placed near high-rise buildings using the statistical property of the reflected and diffracted waves of global positioning system (GPS) signals. First, it is assumed that a GPS signal that contains a reflected and diffracted wave is denoted by the sum of the true position information and noise that follows a time-varying Gaussian distribution. On the basis of this assumption, the time-varying bias of a GPS signal is tracked using a Kalman filter. In addition, a particle filter, which executes sampling and likelihood evaluation using the estimated bias, is developed. With the proposed method, a GPS signal that contains the rejected noise introduced by the conventional method can be used efficiently, and the state estimation accuracy of the robot in a shadow area of GPS satellite can be improved. Furthermore, a control system for an autonomous mobile robot incorporating the proposed state estimation mechanism is developed, and its effectiveness is evaluated via simulation.     

Cooperative Localization of AUVs Using Moving Horizon Estimation

This paper studies the localization problem of autonomous underwater vehicles (AUVs) constrained by limited size, power and payload. Such AUVs cannot be equipped with heavy sensors which makes their underwater localization problem difficult. The proposed cooperative localization algorithm is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper is twofold: 1) The observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. It is further compared with observability of linearized system to verify that a nonlinear state estimation is necessary. 2) Moving horizon estimation is integrated with extended Kalman filter (EKF) for three dimensional localization using single beacon, which can alleviate the computational complexity, impose various constraints and make use of several previous range measurements for each estimation. The observability and improved localization accuracy of the localization algorithm are verified by extensive numerical simulation compared with EKF.      

基于IMU紧耦合的LeGO-LOAM改进算法研究

基于LiDAR和SLAM(simultaneous localization and mapping)的LeGO-LOAM算法在低分辨率的LiDAR设备上,由于LiDAR数据的运动畸变、采样的地面数据稀疏等问题,存在重力矢量漂移现象和严重的高程估计误差。为了改善这一点,LeGO-LOAM改进算法引入了一种LiDAR和IMU(inertial measurement unit)紧耦合的方式。通过IMU估计运动状态,消除LiDAR数据的运动畸变,并使用IMU数据构建联合优化函数,约束位置姿态估计的重力方向。实验结果表明,这种方法有效抑制了LeGO-LOAM算法的重力矢量漂移,高程估计精度和高速状态下的定位精度均有显著提升。     

Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation

Skid-steered mobile robots are widely used because of their simple mechanism and high reliability. Understanding the kinematics and dynamics of such a robotic platform is, however, challenging due to the complex wheel/ground interactions and kinematic constraints. In this paper, we develop a kinematic modeling scheme to analyze the skid-steered mobile robot. Based on the analysis of the kinematics of the skid-steered mobile robot, we reveal the underlying geometric and kinematic relationships between the wheel slips and locations of the instantaneous rotation centers. As an application example, we also present how to utilize the modeling and analysis for robot positioning and wheel slip estimation using only low-cost strapdown inertial measurement units. The robot positioning and wheel slip-estimation scheme is based on an extended Kalman filter (EKF) design that incorporates the kinematic constraints for accuracy enhancement. The performance of the EKF-based positioning and wheel slip-estimation scheme are also presented. The estimation methodology is tested and validated experimentally on a robotic test bed.     

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。