A novel order reduction method for nonlinear dynamical system under external periodic excitations

The concept of approximate inertial manifold (AIM) is extended to develop a kind of nonlinear order reduction technique for non-autonomous nonlinear systems in second-order form in this paper.Using the modal transformation,a large nonlinear dynamical system is split into a 'master' subsystem,a 'slave' subsystem,and a 'negligible' subsystem.Accordingly,a novel order reduction method (Method I) is developed to construct a low order subsystem by neglecting the 'negligible' subsystem and slaving the 'slave' sub...     

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

In this paper, an integration system is proposed to improve the positioning performance of a mobile robot by fusing a Pseudolite Ultrasonic System (PUS), an absolute position measurement system using direct ultrasonic waves, with a Dead Reckoning (DR) odometer. As an integration algorithm of the absolute position measurement system and DR, two methods are proposed. In the loosely coupled method, the PUS and the DR calculate the position independently and a Kalman filter estimates the position using position information from the PUS and the DR. In the tightly coupled method, the PUS provides the distance between the ultrasonic transmitters and receivers without calculating the position directly and the DR provides the translational and rotational displacement of the mobile robot. The Kalman filter then estimates the position using information from the PUS and the DR. In addition, to improve the positioning performance in case the line-of-sight (LOS) between the ultrasonic transmitter and receiver is blocked due to obstacles, a positioning failure detection algorithm and reckoning methods are proposed. The positioning performances of the proposed PUS/DR integrated systems and the validity of the positioning failure detection algorithm are verified and evaluated by experiments.     

基于多传感器数据融合的室内行人航位推算法

针对惯性测量单元(IMU)本身存在测量漂移,很难获得精确的室内行人轨迹的问题,提出了使用多个传感器信息融合的方案,包括IMU和视频摄像头,并结合卡尔曼滤波和零速检测算法进行参数优化,以提高行人运动轨迹的精度.仿真结果表明:算法可以有效降低行人轨迹的误差.     

图像末制导系统中攻击目标点选取方法

反跑道图像末制导系统中,针对弹载图像中可能不存在完整跑道,而跑道内部比较均匀很难由灰度或结构特征锁定目标点的问题,提出一种攻击目标点的选取方法.结合惯导和图像信息,通过一个中间坐标系,估计前帧目标点在当前图像中的位置,过该像点作跑道中线的垂线,垂足定为系统当前的攻击目标点.仿真实验结果表明,该方法能在跑道图像中选取合适的攻击目标点、随着图像分辨率的提高目标越来越精确,并且对惯导误差不敏感,具有鲁棒性.     

MEMS惯性传感器ADIS16355在姿态测量中应用研究

为了实现惯性导航控制,需获取控制对象的姿态角信息,设计了基于MEMS惯性传感器集成模块ADIS16355的姿态测量系统。该姿态测量系统采用ADIS16355作为惯性测量单元,利用加速度计对重力向量的观测来修正陀螺给出的姿态信息,卡尔曼滤波实现传感器信息融合以计算运动载体的姿态角。介绍了ADIS16355的基本功能模块,阐述了两种传感器融合测量实时姿态角的方法并给出了卡尔曼滤波算法迭代过程,基于ARMv7架构的Cotex-M3微处理器设计了姿态测量系统硬件。采用AHRS500GA对该姿态测量系统性能进行了测量姿态角的验证实验,测试结果表明,该姿态测量系统能在动态条件下准确地测定运动物体实时姿态角,其误差一般在?1?左右。     

基于SVR 的惯性/卫星组合导航系统故障诊断方法

在惯性/卫星组合导航系统中, 针对传统X² 检验法检测出故障但无法准确识别故障子系统的不足, 提出一种基于支持向量回归的故障诊断方法. 采用残差X² 检验法实时对组合导航系统进行故障检测, 并构建基于支持向量机的回归预测模型, 实现对惯性导航系统状态的预测; 根据系统模型输出和预测模型输出之差辅助进行惯性导航系统的故障判别, 诊断出系统故障源. 仿真结果表明, 所提出的方法能够快速准确地识别故障子系统, 并进行有效的系统隔离和重构, 从而使组合导航系统的性能得到保障.

     

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

In this paper, we address the problem of ego-motion estimation by fusing visual and inertial information. The hardware consists of an inertial measurement unit (IMU) and a monocular camera. The camera provides visual observations in the form of features on a horizontal plane. Exploiting the geometric constraint of features on the plane into visual and inertial data, we propose a novel closed form measurement model for this system. Our first contribution in this paper is an observability analysis of the proposed planar-based visual inertial navigation system (VINS). In particular, we prove that the system has only three unobservable states corresponding to global translations parallel to the plane, and rotation around the gravity vector. Hence, compared to general VINS, an advantage of using features on the horizontal plane is that the vertical translation along the normal of the plane becomes observable. As the second contribution, we present a state-space formulation for the pose estimation in the analyzed system and solve it via a modified unscented Kalman filter (UKF). Finally, the findings of the theoretical analysis and 6-DoF motion estimation are validated by simulations as well as using experimental data.     

移动USBL测距辅助的UUV协同导航定位方法

针对无人水下航行器(UUV) 导航精度受惯性导航(INS) 影响较大的问题, 本文提出一种基于无人水面船 (USV)携带超短基线(USBL)对UUV进行移动式辅助导航定位的方法. 文中以USV上高精度INS和全球导航卫星系 统(GNSS)组合后的导航结果作为基准, 利用USBL测量得到的USV和UUV相对位置和姿态信息, 结合UUV的INS误 差方程, 建立了UUV协同导航系统的状态方程和观测方程, 并基于自适应卡尔曼滤波方法对UUV状态进行滤波估 计. 仿真和湖上实验结果表明, 文中所提方法可有效提升UUV导航精度.     

Interval set‐membership estimation for continuous linear systems

This article proposes a mixed interval set‐membership estimation (ISME) method for continuous linear time‐invariant (LTI) systems by combining the positive system theory and the set theory. The proposed ISME method gives a new mixed interval‐set estimation framework for continuous LTI systems, whose benefit consists in that it has potential to achieve a balance of computational complexity and robust state estimation conservatism with respect to the interval observer (IO) and the set‐valued observer (SVO) for continuous LTI systems. Particularly, the proposed ISME method first uses a coordinate transformation such that the original system is transformed into an equivalent system. Second, the equivalent system is partitioned into two subsystems, where the first subsystem has a Meztler and Hurwitz subsystem matrix and then an IO is designed for the first subsystem based on the positive system theory. Because it is not guaranteed that the second subsystem also has a Meztler and Hurwitz subsystem matrix, a zonotopic SVO is further designed for the second subsystem based on the set theory. Consequently, an integration of the two steps above provides the whole SE results for the original system. At the end of this article, an example is used to illustrate the effectiveness of the proposed ISME method.     

Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments

This paper presents a hierarchical simultaneous localization and mapping(SLAM) system for a small unmanned aerial vehicle(UAV) using the output of an inertial measurement unit(IMU) and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter(EKF) for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system(GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.     

捷联惯性测量装置角速度通道动态特性辨识

动态特性是评价捷联惯性测量装置的核心技术指标.以某一型号光纤捷联惯性测量装置(含减振器)为研究对象,通过角振动试验和数据分析,结合频域子空间算法与遗传算法,建立了基于捷联惯性测量装置整机角速度通道传递函数模型,为捷联惯性测量装置整机动态性能应用评估奠定了技术基础,方法得到了比传统方法更好的结果,更适合实际工程应用.     

MEMS-IMU /GPS组合导航系统中坐标系统一的方法研究

针对MEMS-IMU/GPS组合导航系统数据融合时坐标系不统一的问题,提出了一种由GPS坐标系(WGS-84坐标系)到惯性坐标系(当地水平游移坐标系)的转换方法。该方法在由GPS坐标系转换到西北天惯性坐标系的基础上,计算与西向的游移偏航角,最终完成坐标系的统一。经实际的跑车实验验证,由GPS信息转换到惯性坐标系下的偏航角与惯性偏航角偏差小于1°,精确的实现两坐标系的统一,在实际的工程应用中具有一定的参考价值。     

点线特征融合的激光雷达单目惯导SLAM系统

多传感器融合的SLAM系统定位精度相比单一传感器的SLAM系统更高,但在低纹理场景或退化场景下的定位精度有待提高。提出一种点线特征融合的激光雷达视觉单目惯导紧耦合SLAM系统（PL2VI-SLAM）,其由点线特征融合的视觉惯导系统（PLVIS）和激光雷达惯导系统（LIS）两个子系统组成。通过PLVIS系统实现点线特征的提取与匹配,使用滑动窗口选择性地引入关键帧,并将惯性导航器件与相机紧耦合以解算位姿。LIS系统将多个约束集成到因子图中进行联合优化,其初始化状态可以作为PLVIS的初始猜测,通过扫描匹配实现激光雷达里程计,并将点云深度分别与PLVIS系统的特征点以及特征线进行关联,为视觉特征提供精确的深度值,提升定位精度。此外,两个子系统将联合进行回环检测,并对位姿进行矫正。在jackal、handled以及自制的长走廊数据集上的实验结果表明,与LVI-SAM、VINS-MDNO及LIO-SAM系统相比,该系统的定位精度更高,适用于低纹理场景及退化场景,并能满足实时性要求。     

MEMS惯性传感器ADIS16355在姿态测量中的应用

设计了基于微电子机械系统(Microelectro mechanical system,MEMS)惯性传感器集成模块ADIS16355的姿态测量系统。该姿态测量系统采用ADIS16355作为惯性测量单元,利用加速度计对重力向量的观测来修正陀螺给出的姿态信息,卡尔曼滤波实现传感器信息融合以计算运动载体的姿态角。介绍了ADIS16355的基本功能模块,阐述了两种传感器融合测量实时姿态角的方法并给出了卡尔曼滤波算法迭代过程。基于ARMv7架构的Cotex-M3微处理器设计了姿态测量系统硬件。采用AHRS500GA对该姿态测量系统性能进行了测量姿态角的验证实验。测试结果表明,该姿态测量系统能在动态条件下准确地测定运动物体实时姿态角,其误差一般在±1°左右。     

Indoor low-cost localization system for controlling aerial robots

This paper presents a low-cost localization system to guide an Unmanned Aerial Vehicle (UAV) in indoor flights, considering an environment with invariant texture and typical indoor illumination. The first contribution of the paper is the proposal of a system to estimate the position and orientation of the UAV, through a multi-sensor fusion scheme, dealing with data provided by a RGB-D sensor, an inertial measurement unit (IMU), an ultrasonic sensor and optical flow-based velocity estimates. A second contribution of the paper is the proposal of a high-level control system to guide the UAV in path-following tasks, involving two controllers: a kinematic one, responsible for generating reference velocities for the vehicle, and a PD one, responsible for tracking such reference velocities, thus characterizing a cascade controller. Experiments with such a localization and control systems, during which abrupt disturbances are applied, were carried out to check the effectiveness of the developed capture and control systems, whose results validate the proposed framework.     

Fusing Visual and Inertial Sensors with Semantics for 3D Human Pose Estimation

International Journal of Computer Vision - We propose an approach to accurately estimate 3D human pose by fusing multi-viewpoint video (MVV) with inertial measurement unit (IMU) sensor data,...     

基于可穿戴传感器的普适化人体活动识别

为了提高日常活动识别的准确性和自动化程度,减少人为干预,提出了利用可穿戴传感信号作为输入,通过深度神经网络进行人体活动识别的方法.首先,设计了普适环境下人体活动识别的系统架构,建立了一套加速度、生理信号等传感数据的采集系统;然后,对获取的传感数据进行降噪、加窗与归一化等预处理,并设计了长短时记忆递归神经网络来进行特征的自动提取和融合,从而实现活动识别.实验结果表明,该方法减少了对人工和专家知识的依赖,自动进行多模态传感器的融合,智能化程度高,分类效果好.     

用于无人机室内导航的光流与地标融合方法

针对小型无人机在无卫星导航信号条件下的导航问题,结合光流及地标定位设计了使用摄像头、惯性测量器件、超声测距仪等传感器融合的无人机室内导航方法.文章使用补偿角速率的光流微分法计算帧间像素点小位移,并用前后误差算法提取精度较高的点,避免像素点跟踪错误,提高了光流测速的精度;对得到的光流场用均值漂移算法进行寻优,得到光流场直方图峰值,以此计算光流速度.本文提出了无累积误差的连续地标定位算法,实时测量无人机位置.通过多速率卡尔曼滤波器对观测周期不一致的位置、速度信息进行最优估计.在搭建的八旋翼无人机平台上试验,将位置与速度测量结果分别与激光和PX4FLOW数据对比,结果表明该导航方法可以有效抑制定位跳变与光流测量噪声误差,给出精确的位置与速度估计.     

Constant-round zero-knowledge proofs of knowledge with strict polynomial-time extractors for NP

Barak and Lindell showed that there exist constant-round zero-knowledge arguments of knowledge with strict polynomial-time extractors.This leaves the open problem of whether it is possible to obtain an analogous result regarding constant-round zero-knowledge proofs of knowledge for NP.This paper focuses on this problem and gives a positive answer by presenting a construction of constant-round zero-knowledge proofs of knowledge with strict polynomial-time extractors for NP.     

Boundary Control for a Flexible Inverted Pendulum System Based on a PDE Model with Input Saturation

This research considers the control problem of a flexible inverted pendulum system (FIPS) in the presence of input saturation. The model for a flexible inverted pendulum system (FIPS) is derived via the Hamilton principle. The FIPS model is divided into a fast subsystem and a slow subsystem via the singular perturbation method. We introduce an auxiliary system to deal with the input saturation of a fast subsystem. To stabilize the fast subsystem, a boundary anti‐windup control force is applied at the free end of the beam. It is proven that the closed‐loop subsystem is stable. For the slow subsystem, a sliding mode control method is employed to design a controller and a new decoupling method to design the sliding surface. Then it is shown that the slow subsystem is stable. Finally, simulation results are provided to confirm the efficacy of the proposed controller.