Standard Dual Quaternion Optimization and Its Applications in Hand-Eye Calibration and SLAM

Several common dual quaternion functions,such as the power function,the magnitude function,the 2-norm function,and the kth largest eigenvalue of a dual quaternion Hermitian matrix,are standard dual quaternion functions,i.e.,the standard parts of their function values depend upon only the standard parts of their dual quaternion variables.Furthermore,the sum,product,minimum,maximum,and composite functions of two standard dual functions,the logarithm and the exponential of standard unit dual quaternion functions,are still standard dual quaternion functions.On the other hand,the dual quaternion optimization problem,where objective and constraint function values are dual numbers but variables are dual quaternions,naturally arises from applications.We show that to solve an equality constrained dual quaternion optimization(EQDQO)problem,we only need to solve two quaternion optimization problems.If the involved dual quaternion functions are all standard,the optimization problem is called a standard dual quaternion optimization problem,and some better results hold.Then,we show that the dual quaternion optimization problems arising from the hand-eye calibration problem and the simultaneous localization and mapping(SLAM)problem are equality constrained standard dual quaternion optimization problems.

Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced,and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure.

Research on Hand-eye Calibration Technology of Visual Service Robot Grasping Based on ROS

For the hand-eye calibration of the vision service robot,the traditional hand-eye calibration tech-nology can’t be realized which because the service robot is independently developed and there is no teaching device to feed back the pose information of the service robot in real time.In this paper,a hand-eye calibration method based on ROS(Robot Operating System)is proposed.In this method,ROS system is used to accurately control the arm of the service robot to rotate in different positions for many times.Meanwhile,the head camera of the service robot takes images of a fixed point in the scene.Then,the nonlinear equations were established ac-cording to the homography matrix of the two images and the position and pose information of the ROS system,and the accurate hand-eye relationship was optimized by the least square method.Finally,an experimental platform is built and the proposed hand-eye calibration method is verified.The experiment results show that the method is easy to operate,simple algorithm and correct result,which verifies the effectiveness of the algorithm and provides conditions for the realization of humanoid grasping of visual service robot.

Online asymmetry estimation for whole angle mode coriolis vibratory gyroscopes by high frequency injection

The whole angle mode gyroscope(WAMG)is considered to be the next generation architecture,but it is suffered from the asymmetry errors to conduct real products.This paper proposes a novel high frequency injection based approach for the error parameters online identification for the WAMG.The significance is that it can separate physical and error fingerprints to enable online calibration.The nonlinear WAMG dynamics are discretized to meet the requirement of numerical precision and computation efficiency.The optimized estimation methods are then constructed and compared to track asymmetry error parameters continuously.In the validation part,its results firstly prove that the proposed scheme can accurately identify constant asymmetry parameters with an overall tracking error of less than 1 ppm and the extreme numerical convergence can reach 10^(-12)ppm.Under the dynamic asymmetry variation condition,the root mean square errors(RMSE)indicate that the tracking accuracy can reach the level of10^(-3),which shows the robustness of the proposed scheme.In summary,the proposed method can effectively estimate the WAMG asymmetry errors online with satisfied performance and practical values.

Hierarchical Bayesian Calibration and On-line Updating Method for Influence Coefficient of Automatic Dynamic Balancing Machine

Measurement error of unbalance＇s vibration response plays a crucial role in calibration and on-line updating of influence coefficient（IC）. Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process, an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed. During calibration process, for the repeatedly-measured data obtained from experiments with different trial weights, according to the fact that measurement error of each sensor had the same statistical characteristics, the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established. During the updating process, information obtained from calibration was regarded as prior information, which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating. Moreover, Gibbs sampling method of Markov Chain Monte Carlo（MCMC） was adopted to obtain the maximum posterior estimation of parameters to be estimated. On the independent developed dynamic balancing testbed, prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively, the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy; the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process, and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.

Development of a Calibration and Monitoring System for GD-1 High Pressure Common Rail Diesel Engine

Based on CAN calibration protocol, a new calibration and monitoring system was developed for the GD-1 high pressure common rail diesel engine. CAN driver block, monitoring program and calibration program for this system were designed respectively. The inquiry mode was used in the monitoring program and the interrupt mode was used in calibration program. The calibration program was designed in structural programming model. This system provides a reliable, accurate and quick CAN bus between ECU and PC, with baud rate up to 500Kbit/s. The implementation of the compatible and universal CAN calibration protocol makes it easy to displace the system and its function modules. It also provides friendly, compatible and flexible calibration interface, and the functions of online calibration and real-time monitoring. This system was successfully used in a GD-1 high pressure common rail diesel engine and the engine performance and exhaust emissions were significantly improved.

IABC: An iteratively automatic machine learning boosted hand-eye calibration method for laser displacement sensor

An on-machine measuring(OMM)system with a laser displacement sensor(LDS)is designed for measuring free-form surfaces of hypersonic aircraft’s radomes.To improve the measurement accuracy of the OMM system,a novel Iteratively Automatic machine learning Boosted hand-eye Calibration(IABC)method is proposed.Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC.Firstly,a new objective function is derived,containing analytical parameters of the handeye relationship and LDS errors.Then,a hybrid calibration model composed of two kernels is proposed to solve the objective function.One kernel is the analytical kernel designed for solving analytical parameters.Another kernel is the automatic machine learning(AutoML)kernel designed to model LDS errors.The two kernels are connected with stepwise iterations to find the best calibration results.Compared with traditional methods,hand-eye experiments show that IABC reduces the calibration RMSE by about 50%.Verification experiments show that IABC reduces the measurement deviations by about 25%-50%and RMSEs within 40%.Even when the training data are obviously less than the test data,IABC performs well.Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.

Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile platform.Calibration is a time-consuming process,and mechanical distortion will cause extrinsic calibration errors.Therefore,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic calibration.In the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different frequencies.In nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic calibration.Extensive experiments were carried out on both public datasets and real-world scenarios.Results indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

一种基于标准样品浓度稀释的在线氚监测仪校准装置

为解决在线氚监测仪的现场校准问题,开展了在线氚监测仪现场校准方法研究,主要包括现场校准装置的机械构造、工作原理、校准装置量值的确定,以及需要开展的校准项目。利用该现场校准装置和氚活度浓度标准装置,对同一台在线氚监测仪进行了校准比较,结果表明两者的校准因子分别为1.07和1.05,相对偏差约为2%,该现场校准装置具备量值传递的能力,可满足现场校准要求。此外,该校准装置具有便携性、可操作性和可拆分的特点,可保证现场校准不受场所作业空间的限制,适合在现场进行校准实验,可解决国内在线氚监测仪设备无法溯源的问题。

微小气体流量在线校准装置的研制

该文研制了一种微小气体流量在线校准装置,可用于工业生产、医疗卫生、环境监测等领域中不便于拆卸的微小气体流量被测仪器的在线校准。详细介绍了微小气体流量在线校准装置的原理、装置的构成、控制系统的设计及校准操作流程,最后通过性能测试验证了校准装置的准确度和可靠性,最后探讨了该装置的应用前景。

基于改进LSTM网络的无人机MEMS-IMU零偏在线标定方法

针对在卫星信号拒止、视觉系统退化场景中无人机MEMS-IMU零偏无法准确估计并补偿导致导航误差迅速发散的问题,提出一种基于改进长短时记忆(LSTM)网络的零偏在线标定方法。首先,为解决MEMS-IMU零偏数据非线性强、传统循环时间网络训练效果差的问题,设计序列到序列的LSTM神经网络结构,引入教师强迫机制,提高了网络特征学习能力。然后,在导航过程中使用训练后的网络对MEMS-IMU零偏在线标定,补偿后的IMU量测与视觉信息联合优化,保证了导航定位精度。实验结果表明,在纯惯性导航实验中,所提方法的绝对位置误差比传统LSTM方法减小了6.5%;在EUROC数据集下进行的视觉惯性组合导航实验中,所提方法的平均绝对位置误差比传统LSTM方法减小了15%。

基于IMU在线校准的SLAM精度提升方法研究

基于多传感器融合的SLAM算法是当前移动机器人实现精准定位和导航的关键技术之一,在分析了惯性测量单元(IMU)的系统误差会对SLAM算法的建图和导航精度影响较大,并且IMU的系统误差会随工作时间发生变化的问题后,提出了一种易于实现的IMU在线标定方法。该标定方法首先建立了IMU中加速度计和陀螺仪的误差模型。其次通过二维云台实现IMU的姿态调整。然后通过读取加速度传感器的静态数值实现加速度计的误差参数辨识。最后以标定后的加速度计输出数据和陀螺仪计算的旋转矩阵,实现陀螺仪的误差参数辨识。为验证以上标定算法的效果,文中基于标定前后的IMU分别进行SLAM建图实验,实验结果表明,基于标定后IMU的建图精度更好。

CD-CAT中基于SCAD惩罚和EM视角的在线标定方法开发——G-DINA模型

G-DINA(the generalizeddeterministic input,noisy and gate)模型限制条件少,应用范围广,满足大量心理与教育评估测验数据的要求。研究提出一种适用于G-DINA等模型的同时标定新题Q矩阵与项目参数的认知诊断计算机化自适应测验(CD-CAT)在线标定新方法SCADOCM,以期促进CD-CAT在实践中的推广与应用。本研究分别基于模拟题库以及真实题库进行研究,结果表明:相比传统的SIE方法,SCADOCM在各实验条件下均具有较为理想的标定精度与标定效率,应用前景较好;SIE方法不适用于饱和的G-DINA等模型,其各实验条件下的Q矩阵标定精度均较低。

基于多传感器融合的无人舰载机舰面定位研究

无人舰载机在航母舰面的自主定位是实现自主转运、提高驶入/驶出效率的重要前提.其中,如何依靠机载传感设备实现GPS拒止环境下的舰载机自主舰面定位是亟待解决的关键技术.为此,提出了一种基于视觉和激光雷达融合的无人舰载机舰面自主定位算法.该算法通过结合手眼标定和互信息标定方法进行多传感器在线标定,使无人舰载机在面对风浪及航母运行等情况下机身摇晃导致传感器外参标定结果变化后仍能保持稳定运行;引入因子图用于无人舰载机的多传感器位姿联合优化并基于自主转运过程中的运动模型建立了简单高效的传感器失效标准,从而有效地融合了激光雷达和视觉定位结果,使该算法在单个传感器失效的情况下依然可以进行精确的自主定位;最后建立了基于多传感器的舰载机舰面仿真系统对本文算法进行验证,实验结果表明,本文算法在单个传感器失效的情况下定位误差仍维持在0.2m以内,满足实际应用要求.

A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

自驱移动雷达在线双向流量测验技术在常州石堰闸的应用

自驱移动雷达波在水文测流中的运用越来越广泛,尤其在山区或航运等条件受限的河道,常规接触式设备难以开展流量测验,可以运用雷达这类非接触式设备开展监测。为了新沟河石堰闸水文站双向流量在线测验,研发流向仪,改造双向流量测验硬件和软件,安装自驱移动雷达在线双向流量监测系统,并开展双向流量测验比测率定,进行运行精度和误差分析。研究成果可为新沟河调度运行以及移动雷达波技术运用提供技术参考。

转炉厂能源计量设备在线校准的方法研究

钢铁行业的大部分能源计量器具在安装调试完毕投入运行后,几乎不可能有条件从安装的计量点位拆卸送检,此类问题在工序能耗气体介质的测量场景中尤其突显。基于柳钢转炉炼钢厂能源计量管理需求和应用于工业领域的检测计量技术现状,结合现场能源计量器具的检校要求,对流量计量仪表进行在线校准,以期最大程度降低校准工作对现场生产造成的影响。

基于CINRAD/CC在线自动标定数据的雷达探测精度提升方法的研究

文章利用吉林白山和云南西双版纳(双偏振)两部天气雷达的在线自动标定数据,通过统计指标最大值、最小值、均值、标准差、合格率及不合格数据的数量等方法进行数据的分析和研究,以此查找雷达存在的问题。结果表明吉林白山和云南西双版纳(双偏振)两部天气雷达在运行过程中存在数据缺测、超限等问题。对存在问题进行整改后,标定数据质量有了明显的提升,有效提升了雷达的探测精度。

AMT手动挡锁挡控制策略研究

为实现纯电动商用车机械式自动变速箱(AMT)适应复杂行驶工况,可通过整车控制器(VCU)控制变速箱进行手动挡锁挡控制。通过使用比例积分(PI)调节的控制方式对电机扭矩进行限制,从而将驱动电机的转速控制在限值之内。然后基于控制器局域网(CAN)标定协议(CCP)进行在线标定,截取相应的报文数据进行分析,验证其功能。试验结果显示,文章的控制方法能够实现车辆的手动挡锁挡功能,电机转速波动范围在±20r/min之内,且车身无抖动现象,具有较好的驾驶平顺性。

大口径在线流量计现场校准影响因素分析

对于现场无法拆却的大口径管道流量计,目前大部分企业选择在线校准[1]的方法来实现量值溯源,故现场校准的准确与否直接影响着其正常使用。根据在线计量的实际需求,采用标准表法比对管道材质、管径、壁厚测量、输入误差、内衬、示值误差读数、磁场因素、流体温度和前后直管段长度等因素对大口径在线流量计现场校准的影响进行了研究,并提出了相应的改进措施,为大口径在线流量计的现场校准提供了一定的指导。