六轴IMU补偿的磁力计动态稳定校准

磁力计是用于确定机器人姿态的常用传感器之一,在使用过程中极易受到周围环境磁场和量测噪声的干扰.传统的椭球拟合能够校准环境磁场的影响,但是不能抑制量测噪声,且不能实时运行,最新的陀螺仪补偿算法弥补了这些缺点,但是由于陀螺仪存在漂移,所以也会使得磁力计数据发生漂移.文中提出一种使用六轴IMU来补偿校准磁力计的方法,首先通过...     

一种支持网络协议的嵌入式敏捷化智能维护单元的实现

基于敏捷制造对制造过程管理与维护的需求,研究并建立了制造系统智能维护单元的原型.针对CNC机床类设备在线监测与诊断系统应用中的问题,提出了支持网络协议的敏捷化多参数集成智能维护单元解决方案,设计了基于智能维护单元的旋转机械监视诊断与维护系统,该系统可在嵌入式处理器ARM和DSP处理器的控制下完成故障诊断和远程维护功能.     

捷联IMU信号采集系统的集成化设计

针对传统的IMU信号采集系统尺寸大、成本高和集成度低等不足,介绍了以DSP为核心的集成式IMU信号采集系统的解决方案,详细阐述了系统硬件实现方案和软件实时误差校正算法.该系统具有速度快、体积小、功耗低等特点.     

应用自回归模型的IMU误差系数漂移预测研究

将影响IMU误差系数漂移并难于分离的多种因素综合为一种因素,假定IMU误差系数的漂移由一特定系统所引起,从而采用时间序列建模方法进行IMU误差系数漂移的预测.该方法具有消除各因素具体分析中的不确定性并减少其工作量的优点.本文描述了其原理,对某惯组的33个误差系数进行了时间序列建模与预测,并得到了有效的结果.最后,阐述了存在的问题及其解决方法.     

Relationship analysis between body flexion angles and smartphone tilt during smartphone use

Although smartphones are used as essential devices in everyday life, many users are exposed to joint diseases owing to prolonged use. The objectives of this study were to analyze how posture and smartphone tasks affect various body flexion angles and develop an algorithm to classify posture/task and estimate body flexion angles using smartphone tilt data. Eighteen participants performed two tasks (playing a game and reading news) in two postures (sitting and standing) in a laboratory environment. The three-axis orientation data (azimuth, pitch, and roll) of the smartphone and the participants’ body flexion angles were measured simultaneously. This study found that the cervical, thoracic, lumbar, and overall flexion angles were all statistically significantly different depending on the posture of the smartphone user, and the cervical flexion angle was significantly different depending on the task. Furthermore, task and task × posture can be classified with high accuracy based on smartphone tilt data, and tilt data had a high correlation with body flexion angles. Relevance to industry: The results of this study can be used as a reference for designing various products and interfaces for neck health. The results can be applied as a smartphone alarm or a built-in application, which can inform the user of the need to stretch his or her neck.     

运动平台双IMU与视觉组合姿态测量算法

快速、准确地获取被测物体的姿态信息,对于航空航天、工业生产等领域都具有重要意义。相较于单IMU测量物体相对于惯性坐标系的姿态,在未知平台的运动状态的情况下,采用双IMU实现物体相对于运动平台坐标系的姿态测量。将IMU测量速度快、短期精度高的特点与视觉测量误差不随时间发散的特点相结合,研究运动平台上双IMU与视觉组合姿态测量算法。提出了一种多速率自适应拓展卡尔曼滤波（MAEKF）算法来融合IMU与视觉的测量结果。采用正交双矢量标定法与q-method实现惯性测量中的坐标系标定。实验证明,该组合姿态测量算法能快速、准确地测量被测物体相对于运动平台的姿态。     

基于超宽带和惯性传感器融合的人体动作捕捉系统

惯性传感器动作捕捉系统能够非常精确的测量人体每个关节的旋转,但是并不能测量人体的位移,降低了系统的可用性。为此提出了一种超宽带定位系统和惯性传感器的融合算法。该算法结合两种传感器的优点,实现的动作捕捉系统不仅可以捕捉表演者的肢体动作,还能够精确定位用户在表演环境中的空间位置。实验表明在室内环境下该动作捕捉系统具有精确性和连续性。     

基于IMU阵列的标定方法

微电子机械系统(MEMS)技术的发展使惯性传感器行业发生了革命性的变化,这使得生产惯性传感器阵列成为可能。然而,低成本的惯性测量系统会受到比例因子和轴失准误差的影响,从而造成位置和姿态估计的精度降低。在单个IMU校正的基础上,设计了一套基于IMU阵列的标定方法,该标定方法为了解决传统六面法在标定IMU阵列过程中方向激励不足的问题,设计了正20面的校正装置,该标定方法不仅能够估计出IMU阵列中单个IMU的比例因子、轴失准误差和偏置,还能估计出阵列中不同IMU之间的坐标轴对齐误差。通过把标定结果和官方所给的校正参数进行对比,可以得到经过本文所提的IMU阵列标定方法得到的标定结果能够达到工厂标定结果的百分之五十到百分之九十。     

Indoor Tracking by RFID Fusion with IMU Data

Indoor tracking and positioning technology have received significant attention. There are many techniques to track indoor targets, such as Wi‐Fi technology, radio frequency identification (RFID), and inertial navigation technology. However, these technologies cannot accurately track a target with a single sensor due to the uncertainty of the sensors. This paper presents an indoor tracking method using RFID and inertial measurement units (IMU); the estimated trajectory is mainly determined by RFID, and the information about the trajectory based on IMU is added when the estimated trajectory is missing without RFID data. The results show that the fusion estimation algorithm has excellent performance in indoor tracking and is very effective when the target maneuvers, even where some measurements are lost.     

Combining passive visual cameras and active IMU sensors for persistent pedestrian tracking

Vision based pedestrian tracking becomes a hard problem when long-term/heavy occlusion happens or pedestrian temporarily moves out of the visual field. In this paper, a novel persistent pedestrian tracking system is presented which combines visual signal from surveillance cameras and sensor signals from Inertial Measurement Unit (IMU) carried by pedestrians themselves. IMU tracking performs Dead Reckoning (DR) approach utilizing accelerometer, gyroscope and magnetometer. IMU tracking has nothing to do with visual occlusion, so it keeps working even when pedestrians are visually occluded. Meanwhile, visual tracking assists in calibrating IMU to avoid the bias drift during DR. The experimental results show that the IMU and visual tracking are complementary to each other and their combination performs robust pedestrian tracking in many challenging scenarios.