遥测测试中的IMU初始对准问题

本文在分析惯性测量单元(IMU)的解算算法的基础上,利用姿态转动分析了遥测测试中惯性测量单元(IMU)数据解算的初始四元数提取方法,并对其进行推导和验证,仿真结果表明了该方法的有效性。     

列车组合导航系统研究与仿真

提出了一种列车组合导航系统.首先,采用低精度的惯性传感器构成简易惯性测量装置(IMU),设计了该简易IMU的安装结构,并给出了其导航定位解算方法.然后,将简易IMU与GPS构成组合导航系统,分析了IMU和GPS各自的误差源,并建立了组合系统误差模型,从而利用卡尔曼滤波技术设计了IMU/GPS列车组合导航算法.仿真结果表明,该IMU/GPS列车组合导航系统具有精度高、可靠性好、成本低等显著优点,非常适用于列车导航定位.     

Multi-sensor fusion methodology for enhanced land vehicle positioning

It is a main challenge for land vehicles to achieve reliable and low-cost navigation solution in various situations, especially when Global Positioning System (GPS) is not available. To address this challenge, we propose an enhanced multi-sensor fusion methodology to fuse the information from low-cost GPS, MEMS Inertial Measurement Unit (IMU), and digital compass in this paper. First, a key data preprocessing algorithm based on Empirical Mode Decomposition (EMD) interval threshold filter is developed to remove the noises in inertial sensors so as to offer more accurate information for subsequent modeling. Then, a Least-Squares Support Vector Machine (LSSVM)-based nonlinear autoregressive with exogenous input (NARX) model (LSSVM-NARX) is designed and augmented with Kalman filter (KF) to construct a novel LSSVM-NARX/KF hybrid strategy. In case of GPS outages, the recently updated LSSVM-NARX is adopted to predict and compensate for the INS position errors. Finally, the performance of proposed methodology was evaluated with real-world data collected in urban settings including typical driving maneuvers. The results indicate that the proposed methodology can achieve remarkable enhancement in positioning accuracy in GPS-denied environments.     

An IMU/UWB/Vision-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment using 802.15.4a Wireless Sensor Network

Indoor localization of mobile agents using wireless technologies is becoming very important in military and civil applications. This paper introduces an approach for the indoor localization of a mini UAV based on Ultra-WideBand technology, low cost IMU and vision based sensors. In this work an Extended Kalman Filter (EKF) is introduced as a possible technique to improve the localization. The proposed approach allows to use a low-cost Inertial Measurement Unit (IMU) in the prediction step and the integration of vision-odometry for the detection of markers nearness the touchdown area. The ranging measurements allow to reduce the errors of inertial sensors due to the limited performance of accelerometers and gyros. The obtained results show that an accuracy of 10 cm can be achieved.     

SINS/GPS组合导航系统仿真研究

以某载体的规划航迹数据为对象,针对捷联、卫星组合导航系统(SINS/GPS)进行了仿真研究。由规划航迹数据计算出载体的比力和角速度信息,输入至惯性测量器件模型,模型输出激励捷联解算模块,得到惯导系统输出参数;同时对规划数据添加观测噪声模拟GPS测量值。采用相对简单的基于半位置、半速度误差的误差方程作为状态方程,以松耦合方式进行集中式Kalman滤波,给出了SINS单独工作与SINS/GPS组合得到的半位置、半速度误差分布。对各状态的观测度进行了研究,确定了不可观测的状态并给出了部分状态可观测度的时间分布。仿真结果表明,方法正确有效,可对SINS/GPS组合导航系统进行算法验证和方案性评估。     

惯性测量组合在线诊断系统研究

为了对武器中的惯性测量组合实施监视与诊断,提出了一套实时在线的故障诊断系统;该系统利用TEAMS-RT与LabVIEW软件和多信号模型建模,实时采集测试点的信号并进行分析,得出惯性测量组合的工作状态;最后通过Simulink模拟产生故障,对整个系统实行检验,结果表明系统正确地推断出故障;所设计的系统,具有在线、实时的特点,可在惯性测量组合的工作过程中及时发现并隔离故障.     

基于小波降噪预处理的组合导航系统研究

根据离散小波变换可降噪的特点,对捷联惯性导航系统（SINS）中惯性测量元件（IMU）的输出精度进行了研究,再与卫星定位系统（GPS）的输出进行卡尔曼滤波组合,进而达到降噪的目的,最后分别由静态时的实际量测数据以及动态时的理论仿真试验给予结果验证。试验结果表明,采用小波降噪预处理后的组合导航系统各项导航参数的精度都有了明显提高,从而提高了组合导航系统的可靠性。     

利用惯导测量单元确定关键帧的实时SLAM算法

由于嵌入式处理器算力的限制,实时性差一直是视觉惯导同时定位与建图（VI-SLAM）走向实际应用的一个亟待解决的问题,因此提出一种利用惯导测量单元（IMU）确定关键帧的实时同时定位与建图（SLAM）算法,主要分为3个线程：跟踪、局部建图和闭环。首先由跟踪线程通过IMU预积分自适应地确定关键帧,而自适应阈值由视觉惯性紧耦合优化的结果得出;然后仅对关键帧进行跟踪,避免对所有帧进行特征处理;最后利用局部建图线程在滑动窗口中通过视觉惯导光束平差法得到更加精确的无人机位姿,利用闭环线程得到全局一致的轨迹和地图。在数据集EuRoC上的实验结果表明,该算法能在不降低精度和鲁棒性的情况下显著减少跟踪线程耗时,降低VI-SLAM对计算资源的依赖。在实际飞行测试中,该算法能够较实时准确地估计出具有尺度信息的无人机飞行真实轨迹。     

基于IMU的手语识别骨骼模型构建

手语识别属于手势识别的研究范畴。传统的基于数据手套的手语识别方法不能完整捕捉手语的所有要素,无法识别手部与肢体配合的手语动作。 惯性测量单元(IMU)由于体积小、成本低而被越来越多地应用到动作捕捉项目中。借鉴机器人运动学相关知识,提出了基于IMU的手语识别骨骼模型,该模型符合人体生物学特征。模型的构建步骤为首先进行骨骼的选取,然后进行尺寸标定。最后提出了标定模型尺寸的实验方法,使用IMU获得的动作集的数据可以进行求解。     

Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors

This paper presents a method to regenerate lower limb joint angle trajectories during gait cycle by judging human intention using wearable sensor system. Myoelectric signals from user are used to detect the intention of gait initiation and gait phases. Multi-channel redundant fusion technique is implemented to obtain a robust stride time and gait phase calculation algorithm. Joint trajectories corresponding to particular gait events and phases are regenerated using a Radial basis neural network. The network is trained with joint angle data measured by Inertial Measurement Unit (IMU) from users with varying anthropomorphic features. Generated trajectory is adaptive to anthropomorphic as well as gait velocity variation. Contribution of this paper is in development of a wearable sensor system, multi-channel redundant fusion to calculate stride time and an adaptive gait trajectory generation algorithm. The proposed method of trajectory generation is used to regenerate lower limb joint motion in sagittal plane for wearable robotic devices like prosthesis and active lower limb exoskeleton.     

Towards semi-autonomous operation of under-actuated underwater vehicles: sensor fusion,on-line identification and visual servo control

In this paper we propose a framework for semi-autonomous operation of an under-actuated underwater vehicle. The contributions of this paper are twofold: The first contribution is a visual servoing control scheme that is designed to provide a human operator the capability to steer the vehicle without loosing the target from the vision system’s field of view. It is shown that the under-actuated degree of freedom is input-to-state stable (ISS) and a shaping of the user input with stability guarantees is implemented. The resulting control scheme has formally guaranteed stability and convergence properties. The second contribution is an asynchronous Modified Dual Unscented Kalman Filter (MDUKF) for the on-line state and parameter estimation of the vehicle by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU). The MDUKF has been developed in order to experimentally verify the performance of the proposed visual servoing control scheme.     

Metric sensing and control of a quadrotor using a homography-based visual inertial fusion method

The combination of a camera and an Inertial Measurement Unit (IMU) has received much attention for state estimation of Micro Aerial Vehicles (MAVs). In contrast to many map based solutions, this paper focuses on optic flow (OF) based approaches which are much more computationally efficient. The robustness of a popular OF algorithm is improved using a transformed binary image from the intensity image. Aided by the on-board IMU, a homography model is developed in which it is proposed to directly obtain the speed up to an unknown scale factor (the ratio of speed to distance) from the homography matrix without performing Singular Value Decomposition (SVD) afterwards. The RANSAC algorithm is employed for outlier detection. Real images and IMU data recorded from our quadrotor platform show the superiority of the proposed method over traditional approaches that decompose the homography matrix for motion estimation, especially over poorly-textured scenes. Visual outputs are then fused with the inertial measurements using an Extended Kalman Filter (EKF) to estimate metric speed, distance to the scene and also acceleration biases. Flight experiments prove the visual inertial fusion approach is adequate for the closed-loop control of a MAV.     

基于IMU与激光雷达紧耦合的混合匹配算法

针对线、面特征匹配的激光雷达测距与地图构建算法(Lightweight and Ground-Optimized Lidar Odometry And Mapping,LeGO-LOAM)在自动导引运输车(Automated Guided Vehicle,AGV)室内室外实时建图与定位时,易出现激光里程计累积误差大和旋转估计不准确等问题,本工作采用惯性测量单元(Inertial Measurement Unit,IMU)与激光雷达紧耦合的LeGO-LOAM算法,通过IMU为激光雷达提供的初始位姿信息,构建IMU与激光雷达联合误差函数,实现位姿共同迭代优化.其中,对于室外结构化信息较少时,在点对点的迭代最近点算法(Iterative Closest Point,ICP)较高定位精度的基础上,结合LeGO-LOAM算法和ICP算法互补性,进一步提出基于IMU与激光雷达紧耦合的混合匹配算法:当环境中结构信息较多时,激光里程计采用LeGO-LOAM算法,而当环境中结构化信息较少时采用ICP算法.实验结果表明,基于IMU与激光雷达紧耦合的混合匹配算法可有效降低激光里程计相对位姿误差和累积误差,提高AGV小车定位精度以消除部分地图重影.     

Finite Horizon Maximum Likelihood Estimation for Integrated Navigation with RF Beacon Measurements

We develop a Finite Horizon Maximum Likelihood Estimator (FHMLE) that fuses Inertial Measurement Unit (IMU) and radio frequency (RF) measurements over a sliding window of finite length for three‐dimensional navigation. Available RF data includes pseudo–ranges, angles of transmission (AoT), and Doppler shift measurements. The navigation estimates are obtained by solving a finite‐dimensional nonlinear optimization using a primal‐dual interior point algorithm (PDIP). The benefits of the proposed estimation method are highlighted using simulations results comparing the FHMLE approach with an Unscented Kalman Filter (UKF), in a scenario where an aircraft approaches a carrier, with RF measurements from beacons aboard the carrier, and low‐cost IMU measurements aboard the aircraft. When the Geometric Dilution of Precision is large, we found that the FHMLE is able to achieve smaller estimation errors than the UKF, which tends to carry a bias throughout the trajectory.     

Human navigation and mapping with a 6DOF IMU and a laser scanner

This article focuses on human navigation, by proposing a system for mapping and self-localization based on wearable sensors, i.e., a laser scanner and a 6 Degree-of-Freedom Inertial Measurement Unit (6DOF IMU) fixed on a helmet worn by the user. The sensor data are fed to a Simultaneous Localization And Mapping (SLAM) algorithm based on particle filtering, an approach commonly used for mapping and self-localization in mobile robotics. Given the specific scenario considered, some operational hypotheses are introduced in order to reduce the effect of a well-known problem in IMU-based localization, i.e., position drift. Experimental results show that the proposed solution leads to improvements in the quality of the generated map with respect to existing approaches.     

虚拟训练动作获取系统软件设计

针对目前虚拟训练软件常用的输入设备逼真度不足的问题,为虚拟训练软件设计了一种新的人性化接口设备（Human Interface Device,HID）一基于MEMS器件的虚拟训练动作获取系统。系统将微惯性测量原理应用于人体姿态检测,选用微机电系统（Micro Electro Mechanical Systems,MEMS）惯性器件构建微惯性测量单元（Micro Inertial Measurement Unit,MIMU）作为人体训练动作的硬件采集设备;采用VC＋＋设计上位机系统软件,完成人体动作获取与识别算法,系统软件通过软件接口驱动虚拟训练软件。通过对原理样机测试,测试结果表明系统运行稳定可靠,解算精度在3°以内,满足虚拟训练的要求,且其操作体验较鼠标键盘操作更为逼真,沉浸感更强。该研究首次将微惯性测量单元应用于人体动作检测,拓展了MEMS器件的应用领域,极大增强了虚拟训练的真实感和训练效果,具有广阔的市场前景。     

Signal Processing and AI-based Assessment of Rehabilitation Exercises for Diastasis Recti Abdominis

Diastasis Recti Abdominis (DRA) is the separation of abdominal recti muscles which occurs in women during their pregnancy and postpartum time. This is because of the stretching of the linea alba, a fibrous connective tissue on the abdominal wall. The Linea Alba is elastic and retracts back after the delivery of the baby. When this tissue gets overstretched, it loses its elasticity and the gap in the abdominals may not be closed leading to DRA. The motive of this research is to analyze the postpartum rehabilitation for signals from Inertial Measurement Unit (IMU) sensors. The conservative treatment for women who are experiencing DRA is given in the form of physiotherapy. These physiotherapy exercises focus on the recti abdominis muscle to bring back the Linea alba together. It will be a difficult process for the physiotherapist to monitor, whether patients did the exercises correctly or not. If the exercises are not correct, they will not be effective in the reduction of inter-recti distance. This research aims to analyze the effectiveness of IMU signals in classifying the correct and incorrect exercises. It was inferred that the IMU signals are effective in classifying the correct and incorrect exercises with an accuracy of 92%.     

An approach to improve direct sensor orientation using the integration of photogrammetric and lidar datasets

The Direct Sensor Georeferencing (DSG) can co-register both photogrammetric and light detection and ranging (lidar) datasets in a common mapping reference system. This procedure is based on the information (position and orientation) derived from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. Due to inaccurate system mounting parameters, the attitudes and positions are badly calculated. In this scenario, the accuracy of the boresight misalignment angles (physical mounting angles between digital camera and IMU) is critical to obtain models free of y-parallaxes. Therefore, a system calibration procedure is required to improve DSG using photogrammetric and lidar datasets. In this sense, this article aims to develop an approach to improve DSG based on the enhancements of the accuracies of the photogrammetric and lidar datasets integration. For this purpose, one sub-block was extracted at the centre of the entire block. Then, the in situ camera calibration was performed using the sub-block and sets of Lidar Control Points. Sequentially, a boresight calibration was performed using Virtual Lidar Control Points. The quality assessment of the approach was measured in both image and object spaces, using two-ray checkpoints at the entire block. The overall results from the performed experiments showed significant improvements of the Root Mean Square Error of the checkpoint discrepancies computed by direct georeferencing.     

Low computational-complexity algorithms for vision-aided inertial navigation of micro aerial vehicles

This paper presents low computational-complexity methods for micro-aerial-vehicle localization in GPS-denied environments. All the presented algorithms rely only on the data provided by a single onboard camera and an Inertial Measurement Unit (IMU). This paper deals with outlier rejection and relative-pose estimation. Regarding outlier rejection, we describe two methods. The former only requires the observation of a single feature in the scene and the knowledge of the angular rates from an IMU, under the assumption that the local camera motion lies in a plane perpendicular to the gravity vector. The latter requires the observation of at least two features, but it relaxes the hypothesis on the vehicle motion, being therefore suitable to tackle the outlier detection problem in the case of a 6DoF motion. We show also that if the camera is rigidly attached to the vehicle, motion priors from the IMU can be exploited to discard wrong estimations in the framework of a 2-point-RANSAC-based approach. Thanks to their inherent efficiency, the proposed methods are very suitable for resource-constrained systems. Regarding the pose estimation problem, we introduce a simple algorithm that computes the vehicle pose from the observation of three point features in a single camera image, once that the roll and pitch angles are estimated from IMU measurements. The proposed algorithm is based on the minimization of a cost function. The proposed method is very simple in terms of computational cost and, therefore, very suitable for real-time implementation. All the proposed methods are evaluated on both synthetic and real data.     

基于Tsfresh-RF特征提取的人体步态识别算法

惯性传感器(IMU)由于尺寸小、价格低、精度高以及信息实时性强等优点, 在人体运动信息的获取与控制等方面得到广泛应用, 但在步态识别的时间序列特征提取和步态环境数据等方面还存在着明显的局限. 本文针对人体下肢步态识别特征提取的复杂性及适用性差等问题, 提出基于Tsfresh-RF特征提取的人体步态识别新方法. 首先, 利用IMU获取的人体步态数据集, 构建基于Tsfresh时间序列特征提取和随机森林(RF)的人体步态识别算法模型. 其次, 采用该算法对人体不同传感器位置进行实验, 完成爬梯、行走、转弯等9种人体运动步态的识别. 最后, 实验结果表明所提方法平均分类准确率达到91.0％, 显著高于传统的支持向量机(SVM)与朴素贝叶斯(NB)等方法的识别结果. 此外, 本文所提基于Tsfresh-RF特征提取的人体步态识别算法具有很好的鲁棒性, 将为后续下肢外骨骼机器人的控制提供有利依据.