低成本车载组合导航在GPS失效下的算法设计

在低成本MEMS-IMU/GPS车载组合导航应用中,城市中建筑的遮挡会造成GPS失效,此时由于MEMS器件精度较低,导航精度会迅速降低甚至发散.针对此问题,引入车辆的天向和侧向速度约束作为虚拟观测,并且考虑到GPS失效时段由于低精度MEMS-IMU带来的非线性,在不做小角度近似的条件下,推导出非线性速度约束作为虚拟观测,利用非线性UKF滤波算法进行估计,以进一步改善导航性能.车载实验表明,提出的非线性速度约束算法能够有效提高GPS失效时的精度,并且在GPS信号再次有效时能够快速收敛.     

Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

In this paper, a low-cost navigation system with high integrity and reliability is proposed. A high-integrity estimation filter is proposed to obtain a high-accuracy state estimate. The filter utilizes a vehicle velocity constraint measurement to enhance the accuracy of the estimate. Two estimation filters, the extended Kalman filter (EKF) and the extended information filter (EIF), are designed and compared to obtain the estimate of the vehicle state. An instrumentation system that consists of a microcontroller, GPS receiver, IMU, velocity encoder, and Zigbee transceiver is used. The microcontroller provides a vehicle navigation solution at 50 Hz by fusing the measurements of the IMU and GPS receiver using the proposed filter design. Extensive experimental tests are conducted to verify the accuracy of the proposed algorithm. These results are processed with and without the velocity constraints. The estimation accuracy improvement with the addition of the velocity constraints is shown. A more than 16 % reduction in the computational time is demonstrated when using the EIF in comparison to the EKF approach.     

非完整约束下滤波算法在组合导航中的应用

MEMS IMU/GPS组合导航系统的应用环境愈来愈复杂,对其精度的要求也愈来愈高,只使用普通卡尔曼滤波不能满足精度和稳定性要求。针对此问题,将Sage-husa自适应卡尔曼滤波算法和非完整约束应用到前向导航滤波算法和后向导航滤波算法中,并将前向滤波和后向滤波结果加权组合,提出了一种非完整约束下加权组合滤波算法,用于事后IMU/GPS联合解算中,用来提高组合导航的精度。并利用实验室设备进行车载实验,通过实测车载数据解算结果来验证该方法的可行性。实验结果表明非完整约束下加权组合滤波后的经纬度误差小于1.4 m,航向角误差小于1.0°,满足MEMS IMU/GPS车载组合导航系统的精度要求。     

A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation

Vision-aided inertial navigation systems (V-INSs) canprovide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved bycombining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.      

Real-Time Implementation of GPS Aided Low-Cost Strapdown Inertial Navigation System

This work details the study, development, and experimental implementation of GPS aided strapdown inertial navigation system (INS) using commercial off-the-shelf low-cost inertial measurement unit (IMU). The data provided by the inertial navigation mechanization is fused with GPS measurements using loosely-coupled linear Kalman filter implemented with the aid of MPC555 microcontroller. The accuracy of the estimation when utilizing a low-cost inertial navigation system (INS) is limited by the accuracy of the sensors used and the mathematical modeling of INS and the aiding sensors’ errors. Therefore, the IMU data is fused with the GPS data to increase the accuracy of the integrated GPS/IMU system. The equations required for the local geographic frame mechanization are derived. The direction cosine matrix approach is selected to compute orientation angles and the unified mathematical framework is chosen for position/velocity algorithm computations. This selection resulted in significant reduction in mechanization errors. It is shown that the constructed GPS/IMU system is successfully implemented with an accurate and reliable performance.     

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。     

Case study: Inertial measurement unit calibration platform

The Department of Mechanical Engineering and the Avionics Engineering Center at Ohio University are developing an electromechanical system for the calibration of an inertial measurement unit (IMU) using global positioning system (GPS) antennas. The GPS antennas and IMU are mounted to a common platform to be oriented in the angular roll, pitch, and yaw motions. Vertical motion is also included to test the systems in a vibrational manner. A 4‐DOF system based on the parallel carpal wrist is under development for this task. High‐accuracy positioning is not required from the platform since the GPS technology provides absolute positioning for the IMU calibration process. © 2000 John Wiley & Sons, Inc.     

基于UKF的UWB和GPS融合定位算法

智能汽车的发展对高精度定位需求日益显现. 针对汽车在城市建筑群、立交桥等特定环境下, 可见GPS卫星数量下降、车载GPS和惯性测量单元(inertial measurement unit, IMU)组合定位系统中IMU产生积累误差导致不能精确定位问题, 本文提出一种基于无迹卡尔曼滤波(unscented Kalman ...     

Attitude determination algorithm using state estimation including lever arms between center of gravity and IMU

In this paper, an enhanced attitude determination algorithm is proposed to decrease the estimation error by including an additive state variable for the lever arm. Attitude determination generally is carried out by measurements from an IMU (inertial measurement unit), which is typically located at the center of gravity of the vehicle. The IMU lever arm, which spans the distance between the IMU and the center of gravity, causes extra acceleration in the accelerometer and increases the error in attitude estimates. However, if the extra accelerations caused by the lever arm can be removed from the measurements of accelerometers, the increased attitude error caused by the IMU lever arm can be prevented. Because an IMU lever arm is fixed in a vehicle after installation, it can be considered as an additive element of the state vector in Kalman filter for attitude determination. The proposed algorithm is composed of a quaternion-based Kalman filter and includes an estimation of the IMU lever arm. In addition, in order to determine components of lever arm, the gross measure of modal observability is investigated for the system. An evaluation of the proposed algorithm is carried out by simulations with a noise model based on an actual IMU. Evaluations through simulations show that the proposed algorithm improves the performance with regard to errors.     

Vision-based control of near-obstacle flight

This paper presents a novel control strategy, which we call optiPilot, for autonomous flight in the vicinity of obstacles. Most existing autopilots rely on a complete 6-degree-of-freedom state estimation using a GPS and an Inertial Measurement Unit (IMU) and are unable to detect and avoid obstacles. This is a limitation for missions such as surveillance and environment monitoring that may require near-obstacle flight in urban areas or mountainous environments. OptiPilot instead uses optic flow to estimate proximity of obstacles and avoid them. Our approach takes advantage of the fact that, for most platforms in translational flight (as opposed to near-hover flight), the translatory motion is essentially aligned with the aircraft main axis. This property allows us to directly interpret optic flow measurements as proximity indications. We take inspiration from neural and behavioural strategies of flying insects to propose a simple mapping of optic flow measurements into control signals that requires only a lightweight and power-efficient sensor suite and minimal processing power.     

Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm

Recently, methods based on Artificial Intelligence (AI) have been widely used to improve positioning accuracy for land vehicle navigation by integrating the Global Positioning System (GPS) with the Strapdown Inertial Navigation System (SINS). In this paper, we propose the ensemble learning algorithm instead of traditional single neural network to overcome the limitations of complex and dynamic data cased by vehicle irregular movement. The ensemble learning algorithm (LSBoost or Bagging), similar to the neural network, can build the SINS/GPS position model based on current and some past samples of SINS velocity, attitude and IMU output information. The performance of the proposed algorithm has been experimentally verified using GPS and SINS data of different trajectories collected in some land vehicle navigation tests. The comparison results between the proposed model and traditional algorithms indicate that the proposed algorithm can improve the positioning accuracy for cases of SINS and specific GPS outages.     

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.     

低空无人机航空摄影高度自动测量方法研究

为控制低空无人机摄影高度,获得更加清晰的地理信息图像,需要对低空无人机摄影高度自动测量方法进行优化研究;当前方法主要利用射影几何知识的自动化标定方法实现低空无人机航空摄影高度的自动测量;该方法存在噪声影响严重,且测量误差较大的问题;为此,提出一种基于多传感器与卡尔曼滤波相结合的低空无人机航空摄影高度自动测量方法;该方法首先通过分析气压测量法计算各种气压因素对低空无人机航空摄影高度的影响,然后推导出大气对流层内气压随低空无人机航空摄影高度的变化;然后采用双GPS系统同时工作,对GPS、气压高度计和IMU测量获得的低空无人机航空摄影高度信号进行冗余备份;采用基于二阶多项式的修正方法对低空无人机航空摄影传感器输出值进行补偿和修正;根据动力学方程建立低空无人机航空摄影的动力学方程获得高度测量状态方程;最后采用卡尔曼滤波的线性最小方差估计准则对低空无人机航空摄影高度进行均方差估计计算,实现低空高度自动测量与校正。实验结果表明,所提方法具有精度高、收敛性好且滤波效果理想的优势。     

基于DSP的IMU/GPS数据融合算法实现

提出了基于DSP的IMU/GPS数据融合算法的实现方案;鉴于GPS数据稳定性高、误差不随时间积累和IMU数据更新率快、在短时间内精度高的特点,采用强跟踪卡尔曼滤波算法对二者的数据进行融合处理,并且在DSP上将其实现,获取精度更高、稳定性更好的导航参数;给出了详细的设计步骤,进行了大量的静态和动态试验,并且对实验数据进行了分析与对比;结果表明,该实现方案可以获取可实用的导航参数。     

直接法UKF在组合导航中的应用

提出直接法卡尔曼滤波(UKF)应用于GPS/捷联惯导(SINS)组合导航,避免对非线性系统的线性化。选择SINS惯导系统输出位置和速度作为系统状态,GPS输出的导航参数作为观测量,使用IMU提供的姿态,用UKF方法结合反馈法对组合导航参数直接进行估计,不仅可以避免了每次导航复杂的初始对准过程,同时保持参数误差不会无限增大。根据是否出现GPS中断两种情况进行,实验结果表明,可以直接使用IMU提供的姿态对智能清洁船的定位导航。     

山区道路车辆动力学模型与行车安全分析

针对道路的几何线形,特别是纵坡坡度与弯道半径对车辆行驶状态的影响,建立了车路耦合的8自由度山区道路行驶的车辆动力学模型以及Dugoff轮胎力模型.结合车载GPS/IMU的测量信息,解算了不同车轮的滑移率以及垂直载荷,并通过横向载荷转移率(LLTR)对车辆的行驶稳定性进行分析.结果表明:车辆行驶过程中的侧向加速度与道路纵坡坡度以及车辆重心高度与宽度的比率h/T有关,坡度越陡,h/T越大,侧向加速度越大,车辆的行驶稳定性越差,降低车辆的行驶速度与侧向加速度可提高车辆的行驶稳定性.     

低成本传感器数据融合技术辅助iPhone导航研究

多传感器数据融合辅助iPhone导航研究的是一个基于IMU/GPS/磁力计的联合解算导航算法,即将手机中加速度计测量到的比力信息与陀螺仪测量到的角速率信息通过扩展卡尔曼滤波技术进行融合。针对低成本传感器的特性,设计了相对应的初始对准方法。在GPS/INS组合阶段,通过添加载体坐标系下左右方向和竖直方向的载体速度为观测量来增加系统的可观测性,从而达到对状态量的更优估计,最终通过反馈校正得到较为精确的载体位置、速度和姿态信息。     

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.     

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.