一种可参数自标定的鲁棒视觉/惯性定位方法

以视觉/惯性里程计为代表的视觉/惯性定位方法近年来被广泛应用。传统的视觉/惯性里程计通过离线标定方法得到固定的相机畸变参数,当相机畸变参数标定不准确或发生变化时定位精度会下降。针对于此,提出了一种面向相机畸变参数在线自标定的鲁棒视觉/惯性里程计方法:首先,将相机畸变参数加入到视觉/惯性里程计的待优化变量中,推导了视觉重投影误差关于待优化变量的雅可比矩阵;然后通过因子图优化技术,实现相机畸变参数的在线自标定与载体导航信息的实时优化求解;最后,通过EuRoC数据集试验和实际试验验证了所提方法的有效性。实际试验结果表明:相对传统的视觉/惯性里程计方法,所提方法在室外开阔场景中精度提升了65.40%。     

一种基于图优化的行人协同定位方法

基于手机惯性传感器的行人航位推算方法是行人导航的核心方法之一。 然而由于传感器噪声等因素,航位推算获取 的位置信息误差往往随着时间发散,通常将航位推算和卫星导航通过卡尔曼滤波构成组合导航系统,利用卫星提供的高精度定 位信息补偿航位推算误差。 提出一种基于图优化的行人协同定位方法,将状态转移、量测和协同测距信息都作为状态的约束, 统一进行优化估计。 为验证方法的有效性,分别在卫星信号良好、无卫星环境下进行了实验验证。 实验分析结果表明,基于图 优化的行人协同定位方法在有无卫星信号情况下,都可以有效地提升系统的定位精度。 和基于卡尔曼滤波的协同方法相比,最 大水平定位误差都减少了 30% 以上。     

Improving unmanned ground vehicle navigation by exploiting virtual sensors and multisensor fusion

This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed nonholonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e.g., forest canopy and urban canyon).     

融合 WiFi 与可穿戴惯导模块的室内定位方法

为解决基于智能手机的人员室内定位追踪易受手机姿态影响的问题,提出一种融合WiFi与可穿戴惯导模块的室内定位方法。通过固定在胸部的惯性测量单元实现行人航迹推算PDR)定位,消除手机姿态对PDR定位的影响,采用加权贝叶斯算法实现WiFi指纹定位,为PDR提供初始定位,同时基于无迹卡尔曼滤波融合WiFi定位结果与PDR定位结果,以减少PDR的累积定位误差。最后,在真实室内环境中进行大量实验,实验结果证明本文提出的加权贝叶斯WiFi定位算法相比于传统贝叶斯算法定位误差降低了51.9%,提出的融合WiFi与可穿戴惯导模块的定位方法具有更好的精度和稳定性,相比于纯PDR定位算法平均定位误差降低了65.2%,相比于完全利用手机实现的融合算法,在3种不同手机姿态下平均定位误差分别下降了12.3%、39.3%和48.4%。     

GPS/INS组合导航系统的研究

讨论了飞机惯性导航系统(INS)与全球卫星导航系统(GPS)的利与弊以及卡尔曼滤波方法在组合定位中的应用情况,进一步提出了基于神经网络数据融合方法的GPS/INS组合导航系统.系统神经网络结构采用单隐层的三层神经网络,输入输出神经元数目是4个,基于256个训练样本由经验公式求得隐层神经元数目为8个,同时还建立了惯导系统的数学模型和数据融合的数学模型.给出了利用MATLAB编制的神经网络训练程序并对这一神经网络进行了训练和仿真.实验表明,组合导航系统经度误差可达9m,纬度误差可达8m,与单独GPS定位和INS定位相比精度得到了提高.     

IMU转动角速度对旋转SINS的精度影响分析

针对惯性测量单元(inertial measurement unit,IMU)旋转角速度变化过程对旋转调制型捷联系统(strapdown inertial nav-igation system,SINS)定位精度的影响进行分析和研究。例举IMU旋转方式并分析旋转自补偿技术调制惯性器件偏差的基本原理;详细推导了IMU运动状态变化过程对调制型捷联系统导航精度的影响并分析了IMU正反转方案的误差特性,最后根据仿真分析确定旋转角速度的选取依据。在理论分析的基础上进行了仿真实验。结果表明,IMU的旋转运动可以有效地调制惯性器件部分偏差,但是旋转角速度的大小及角速度变化过程依然会对调制型捷联系统的定位精度产生影响。     

铁路物流园内集装箱AGV的导航与精度研究

以国内某铁路物流园内AGV集装箱转运为工程背景,为解决AGV的定位导航问题,设计一种基于视觉ArUco标签与惯性导航系统组合的自主导航系统,通过分析惯性导航系统的位置方程和误差方程,研究误差的主要组成部分,按照实际工况,计算AGV惯性导航系统理论位置误差。根据前摄像头几何成像数学模型、ArUco标签特征点与图像特征点之间的投影变换关系,得出AGV在堆场的确切位置,该位置信息作为惯性导航系统的初始值,对惯性导航系统的累积误差进行修正,实现AGV的粗定位导航。使用AGV俯视摄像头进行装卸集装箱精确定位,计算相机测量误差方程。通过实际测量验证了位置误差理论计算的正确性。该方法为AGV定位导航方案设计、精度计算、传感器选型及布置提供一定的参考。     

不依赖精密转台的 MEMS-IMU 误差标定补偿方法

微机电惯性测量单元(MEMS-IMU)具有尺寸小、重量轻、成本低、可靠性高等优点,在机器人、虚拟现实以及智能穿戴等诸多领域广泛应用。低成本的微机电惯性测量单元在使用过程中受噪声和零偏误差等影响,需要通过测试和误差补偿手段来提高其实际使用精度。本文提出了一种全面测试和补偿惯性测量单元误差的方法,通过建立MEMS-IMU的误差模型,使用优化方法标定误差模型中的系统误差参数;使用Allan方差分析方法确定随机误差参数;基于上述结果,采用与视觉融合的非线性优化方法在线实时估计并补偿零偏,最终达到提高定位精度的目的。通过实验分析,上述组合方法不需要使用专门测试标定设备,能够有效补偿低成本微机电惯性测量单元的误差,提高定位精度。     

基于原子干涉技术的水下重力辅助导航研究展望

重力辅助惯性导航是当前水下潜航器导航定位研究的热点和前沿问题,有望成为下一代水下高精度导航系统发展的重要方向。首先,介绍了水下重力信息对于校正惯导系统误差的重要性,阐述了水下重力辅助惯性导航的基本原理与技术内涵;然后,从无图匹配、有图匹配等不同发展阶段,总结了基于传统相对重力仪的水下重力辅助导航的研究现状及发展趋势;进一步分析了下一代水下自主导航系统对高精度绝对重力测量技术的需求,梳理并讨论了基于原子干涉重力测量技术的最新发展及应用状况,展望了原子干涉重力测量技术在水下惯性导航领域的应用前景并总结了仍需解决的关键技术;最后,给出了我国重力辅助导航研究存在的不足及发展建议。     

垂线偏差对惯导系统位置误差的影响分析

针对制约高精度惯性导航系统精度的垂线偏差误差项问题，研究了垂线偏差对惯性导航系统水平位置误差的影响及各级惯导系统误差补偿时垂线偏差的指标需求。首先，推导了垂线偏差引起的惯导系统误差项的直接差分法和四阶龙格库塔数值更新算法，对比分析了两种算法在不同地区的水平位置误差的更新效果；然后，采用3种分辨率的垂线偏差网格数据对惯导系统进行补偿；最后，分析了垂线偏差补偿频率对位置误差补偿的效果并开展了车载导航DOV补偿实验。仿真及实验结果表明，两种误差更新算法都可以有效计算水平位置误差；垂线偏差最大可引起近3 000 m的位置误差，水平姿态误差与方位姿态误差1 h漂移约18″和72″;经DOV补偿后，水平定位精度提升了约230 m。     

High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders.     

捷联惯导系统误差模型与仿真分析

为研究捷联惯导系统短时间导航精度,建立了导航误差数学模型,分析了惯性器件误差对系统导航精度的影响。应用捷联惯性导航原理,针对系统短时间导航的特点,简化载体在导航坐标系的导航方程;由惯性器件安装误差与陀螺仪等效零漂经过方向余弦矩阵变换建立载体姿态误差方程;结合导航方程、姿态误差方程与惯性器件误差推导出载体速度误差与位置误差数学模型。在此基础上,建立了误差状态空间方程与误差模型框图。在Matlab/Simulink环境下建立了误差数学模型计算模块,用捷联惯导算法与误差模型共同解算地面150秒导航试验数据,结果表明：导航系X轴的相对系统误差小于20%,Y轴、Z轴的相对系统误差小于5%,验证了误差数学模型的正确性。此外,分析了加速度计精度的变化对短时间工作的捷联惯导系统导航误差产生基本的影响。     

One new onboard calibration scheme for gimbaled IMU

In most cases, a gimbaled inertial measurement unit (IMU) can provide more accurate navigation information than a strap-down IMU when there is no navigation aiding of global positioning system (GPS) or other equipments. The gimbaled IMU is still used in self-contained precision navigation systems such as launch vehicles, missiles and vessels.     

ENHANCING KALMAN FILTERING–BASED TIGHTLY COUPLED NAVIGATION SOLUTION THROUGH REMEDIAL ESTIMATES FOR PSEUDORANGE MEASUREMENTS USING PARALLEL CASCADE IDENTIFICATION

Integrated global positioning system (GPS) solutions that utilize micro-electro-mechanical systems (MEMS)-based inertial sensors provide a more accurate navigation solution than stand-alone GPS in challenging scenarios. To keep the integrated solution less affected by sensor errors and to decrease the cost, a reduced inertial sensor system (RISS), which consists of only one gyroscope and two accelerometers, together with an odometer and integrated with GPS, is proposed. Tightly coupled integration is a better choice in demanding scenarios, as it can provide GPS aiding even when the number of visible satellites is three or less. However, inaccuracies of pseudoranges measured by the GPS receiver and used as aiding in the RISS/odometer/GPS integration solution will affect the overall positioning accuracy. This article explores the benefits of using parallel cascade identification (PCI), a nonlinear system identification technique that improves the overall navigation solution by modeling residual pseudorange correlated errors to be used by a Kalman filter (KF)–based tightly coupled RISS/odometer/GPS navigational solution. When less than four satellites are visible, the identified parallel cascade model for the still visible satellites is used to predict the residual pseudorange errors for these respective satellites, and the corrected pseudorange value is provided to KF. The performance of PCI for correcting the pseudoranges is examined and verified using road test trajectories and compared to a traditional tightly coupled RISS/odometer/GPS KF solution. The results demonstrate the advantages of this technique in correcting the pseudoranges and enhancing the positional solution.     

二维里程辅助的掘进机自主导航方法研究

针对掘进工作面恶劣的环境以及掘进机时常发生滑移运动造成掘进机导航参数不能精准获取的难题,提出了基于二维 里程辅助的掘进机自主导航方法。 研制了二维里程测量装置,搭建了二维里程辅助的掘进机自主导航系统;建立了二维里程测 量装置和捷联惯导误差方程,推导了二维里程辅助的组合导航算法。 为了应对量测噪声统计特性时变导致系统估计误差变大 的问题,提出了基于模糊理论的数据融合算法,实时在线调整噪声协方差矩阵。 仿真实验结果表明模糊融合算法相对于传统融 合算法将航向角误差降低了 34. 78% ;3 个方向的定位误差分别降低了 44. 33% 、41. 82% 和 42. 26% 。 进行了掘进机导航定位实 验,实现了 0. 052 m 的定位精度,满足无人掘进工作面对掘进机自主导航的要求。     

基于微型惯性测量组合的大型工件三维位置测量方法

提出了利用微型惯性测量组合进行工件三维位置测量的方法,对这一方法的测量原理进行了详细阐述,对坐标转换、姿态矩阵求解、位置计算等均给出了详细的数学模型及算法。采用普通陀螺及加速度计进行了原理性试验,给出了相应的试验结果。另外,对这一方法进行了误差分析,指出了它不同于一般捷联式系统的特点,分别针对惯性传感器和系统整体给出了相应的误差修正算法,提出了提高测量精度的措施。实际证明,本方法具有实现方便、检测速度快及可实现连续测量等优点,对大型工件及复杂表面的快速测量具有明显得实际意义。     

微惯性捷联导航(MINS)与GPS组合导航的工程实现

简要介绍了MINS/GPS组合导航系统的设计。描述了组合算法和惯性解算算法的实现,分析了样机在实验室的静态实验结果。实验表明,系统组合滤波器的设计和参数合理,样机取得了较好的导航解算精度。     

全方位自动导引车的导航与控制系统研究

为了提高全方位自动导引车(AGV) 的导航精度和实时性,文中对其导航和控制系统进行了深入研究。首先,对基于麦克纳姆轮的全方位AGV 进行了运动学分析,建立了运动学模型;随后,使用卡尔曼滤波对两种不同原理的定位方式进行了融合,弥补了二者各自的不足,在保证定位频率的前提下,提高了定位精度和鲁棒性;使用PID 控制实现了全方位AGV 的路径跟踪,实现了系统的自动控制;最后,在实验中验证了算法的有效性。经过测试,全方位AGV 行驶精度可以达到10 mm,导航和控制系统具有良好的稳定性和实时性。     

A new method of seamless land navigation for GPS/INS integrated system

For the last few years, integrated navigation systems have been widely used to calculate positions and attitudes of vehicles. The strapdown inertial navigation system (SINS) provides velocity, attitudes and position information, whereas the global positioning system (GPS) provides velocity and position information. A method using neural network (NN) and wavelet-based de-noising technology is introduced into the SINS/GPS/magnetometer integrated navigation system, because system accuracy may decrease during GPS outages. When the GPS is working well, NN is trained using the velocity and position information provided by SINS as input and the corresponding errors as output. Wavelet multi-resolution analysis (WMRA) is also introduced to de-noise the errors, the desired output of NN. Test results showed that velocity accuracies improved using NN, but other accuracies remained poor. By re-training NN with WMRA, the system accuracies improved to the level of using normal GPS signal. In addition, NN trained with WMRA also improved the approximation to the actual model, further enhancing alignment accuracy.     

The research of PNS based on micro inertial sensors

In order to reduce the dependence of pedestrian navigation on satellite navigation system and wireless communication, a new method is proposed to construct pedestrian navigation system (PNS) based on micro inertial technology. In this paper, the distributed system structure is arranged on human feet and trunk, and the key technologies, namely the system initial alignment, the error correction, the precise gait-phase detection, the effect and the inhibition of environmental magnetic field, have been studied. Besides, theories and applications of the key technologies are also discussed, and the performance of the PNS has been analyzed in the environment of electromagnetic interference. Experimental results show that the positioning error of the route with electromagnetic interference accounts for 2% of the travel distance. The key technologies proposed in the study can effectively improve the positioning accuracy of PNS, and independently achieve longer time personal navigation in the attenuate or even invalid global navigation satellite system (GNSS) and wireless communication signal environment.