Federated‐filter‐based unmanned ground vehicle localization using 3D range registration with digital elevation model in outdoor environments

An integrated GPS/INS does not guarantee localization robustness in outdoor environments, because GPS is vulnerable to external disturbances. However, a digital elevation model (DEM) contains 3D data on the terrain over a specified area and hence can provide in‐depth localization information during GPS blockage. This paper proposes federated‐filter‐based localization using three‐dimensional (3D) range registration with a DEM. A no‐reset‐feedback method is used and a 3D LIDAR sensor, magnetic compass, and odometer are used to correct INS errors in GPS blockage. For 3D range registration with DEM, this paper presents a framework based on the weighted registration scheme of two transformations, pairwise registration and registration with DEM, with the INS position and attitude information. The transformation is first determined by comparing the results of two registration methods with the INS position and is then modified to replace the orientation result of 3D registration with the INS attitude. A multilayered DEM approach using the height of the integrated system is also used to constrain the search range of DEM into three layers near the current unmanned ground vehicle (UGV) position when the corresponding point is searched for in the DEM. Experimental results show that the proposed localization algorithm can greatly enhance the robustness and accuracy of UGV localization in outdoor environments. © 2012 Wiley Periodicals, Inc.     

Vehicle Localization in Outdoor Mountainous Forested Paths and Extension of Two-Dimensional Road Centerline Maps to Three-Dimensional Maps

This paper presents an approach for vehicle three-dimensional (3-D) localization in outdoor woodland environments where a previously available two-dimensional road centerline map is used in combination with a loosely coupled multi-sensor system to estimate the vehicle position in mountainous forested paths. The localization system is composed of a wheel encoder, an inertial measurement unit, a DGPS, a laser sensor and a barometer. An extended Kalman filter is used for sensor data fusion and pose estimation. When available, DGPS is used for 3-D dead reckoning accumulated error correction. During DGPS blackouts, the laser sensor is used for road extraction and measurement of the displacement of the vehicle to the road centerline, then the position is corrected towards the map. Moreover, the barometer that measures the height difference towards a reference is used to correct the estimated height in absence of DGPS 3-D data. The estimated height is added to the available road map to obtain a 3-D road centerline map that includes the road width measured with the laser sensor. Experimental results in large-scale real mountainous woodland environments show the robustness and simplicity of the proposed approach for vehicle localization and 3-D map extension.     

Differential GPS and odometry-based outdoor navigation of a mobile robot

This paper describes outdoor navigation for a mobile robot by using differential GPS (DGPS) and odometry in a campus walkway environment. The robot position is estimated by fusion of DGPS and odometry. The GPS receiver measures its position by radio waves from GPS satellites. The error of GPS measurement data increases near high buildings and trees because of multi-path and forward diffractions. Thus, it is necessary to pick up only accurate DGPS measurement data when the robot position is modified by fusing DGPS and odometry. In this paper, typical DGPS measurement data observed near high buildings and trees are reported. Then, the authors propose a novel position correction method by fusing GPS and odometry. Fusion of DGPS and odometry is realized using an extended Kalman filter framework. Moreover, outdoor navigation for a mobile robot is accomplished by using the proposed correction method.     

Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering

A combined algorithm for the loosely fused ultra wide band (UWB) and inertial navigation system (INS)-based measurements is designed under the indoor human navigation conditions with missing data. The scheme proposed fuses the INS- and UWB-derived positions via a data fusion filter. Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability, we also consider the missing data problem in UWB measurements. To overcome this problem, the loosely-coupled INS/UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response (UFIR) fusion filter. We show experimentally that, the standard UFIR fusion filter has higher robustness than the Kalman filter. It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.      

Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system

Motivated by the complementary features of the IIR-type filter and the FIR-type filter, this paper proposes a robust IIR/FIR fusion filter and an INS/GPS integrated system designed with the fusion filter. In the fusion filter, an IIR-type filter (SPKF) and a FIR-type filter (MRHKF filter) are processed independently, and then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are integrated and the demerits of the filters are diminished through the filter fusion. Consequently, the proposed fusion filter shows robustness against model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of two filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied to an INS/GPS integrated system. The performance of the INS/GPS integrated system designed using the fusion filter is verified through a simulation under various error environments and is experimentally confirmed.     

无地面基准的相对导航系统设计与仿真

在无地面基准的机群编队飞行JTIDS/INS组合导航系统中,采用新的相对坐标系建立方法克服了绝对位置偏差对相对导航定位精度的影响,同时在原有的TOA观测量的基础上增加了DOA观测量,采用UKF非线性滤波算法进行系统滤波器设计,进一步提高了相对定位精度,并设计了JTIDS/INS组合导航仿真软件平台.仿真结果表明,该方法在收敛速度和定位精度方面都有很好的性能.     

RFID assisted vehicle positioning in VANETs

With technological advancement, recent VANET applications such as safe driving and emergency rescue often demand high position accuracy. Unfortunately, however, conventional localization systems, e.g., GPS, hardly meet new accuracy requirements. To overcome this limitation, this paper proposes an RFID-assisted localization system. The proposed system employs the DGPS concept to improve GPS accuracy. A vehicle obtains two different position data: GPS coordinate from its own GPS receiver and accurate physical position via RFID communication. Then, it computes GPS error and shares it with neighbors to help them correct inaccurate GPS coordinates. To evaluate the proposed system, we conduct extensive experiments both on a simulator and on a real world test-bed. The simulation shows that, with the RFID-assisted localization system, vehicles can acquire accurate position both on a freeway and in an urban area. The results from the test-bed experiments demonstrate that the proposed system is feasible in the real VANET environment.     

融合5G/GNSS的车辆高精度鲁棒安全定位:进展与展望

不受环境和条件影响的准确、实时定位对于基于位置的车辆应用和自动驾驶至关重要.典型车辆定位通常依赖于全球卫星导航系统(GNSS),如美国GPS、中国北斗等,由于易受遮挡和阻塞,常将其与惯导、视觉等技术融合弥补GNSS缺陷.但车规级传感器易受驾驶状态、天气等因素影响,很难精确测量,影响定位性能.近年来,依托先进5G技术和广域基础设施建设,5G/GNSS融合定位可以提供更为精确鲁棒实时的位置结果,并逐渐成为车辆高精定位的主要手段.鉴于极少有车辆定位领域应用5G/GNSS融合方法的系统综述,面向车辆定位,从精度、鲁棒、实时安全等多方面分述基于5G/GNSS融合的先进定位方法,并探讨研究空白和未来研究方向.     

基于UWB和DGPS的混合定位方法研究

超宽带UWB(Ultde Wide Band)定位是近几年发展起来的无线定位技术,UWB信号具有抗多径效应好、定位精度和刷新率高等优点,但UWB一般只能用于进行室内高精度定位。而差分GPS(DGPS)的室外定位精度能达到分米级。描述了结合UWB和DGPS(Differential Global Position System)的定位方法,采用融合Savitzky-Golay滤波器与粒子滤波器来解决不同传感器的数据融合,实现室内外的无缝定位切换,应用在需要室内室外联合定位的场合。实验表明,采用融合不同滤波器进行处理后,能有效地提高定位精度。     

GPS/INS组合导航系统时间同步系统设计

为了说明高动态环境中时间同步对于组合导航系统的重要性,在Kalman滤波方程的基础上,推导了时间同步误差与Kalman滤波结果之间的定性关系.提出一种利用GPS接收机中1PPS(Pulse Per Second)信号作为同步标签的时间同步方法,将IMU中的数据加上精确的时间标签,从而达到时间同步的目的.全部时间同步功能由FPGA实现,利用Verilog HDL语言进行开发,整体硬件结构简单而且适用范围广.试验结果显示了这种时间同步设计可以明显减小滤波结果的估计误差,有效的提高了组合导航系统的定位精度.     

基于因子图算法的INS/GPS信息滞后处理方法

针对全球定位系统（GPS）信息滞后导致惯性导航系统（INS）/GPS组合导航系统实时性差的问题,利用因子图算法可以在一个信息融合时刻处理各信息源不同时刻量测信息的特点,提出了一种INS/GPS信息滞后处理方法。在系统接收到GPS信息之前,因子图模型中只添加关于INS信息的因子节点,经增量推理求出组合导航结果,保证系统的实时性。待系统接收到GPS信息之后,再将关于GPS信息的因子节点添加到因子图模型中,修正INS误差,从而保证系统长时间高精度运行。仿真结果表明,当上一时刻实时导航状态量对INS误差修正效果随GPS信息滞后时间变长而逐渐变差时,可以采用上一时刻刚刚完成量测更新的导航状态量实现INS误差的有效修正。因子图算法在保证系统精度的前提下,避免了GPS信息滞后对INS/GPS组合导航系统实时性的不良影响。     

基于MLP神经网络改进组合导航算法

为解决GPS信号失锁条件下,GPS/INS(inertial navigation system)组合导航系统解算精度降低甚至发散的问题,提出采用多层感知机神经网络(multilayer perceptron neural networks,MLPNN)来辅助组合导航系统.在GPS信号有效时对神经网络进行训练,在GPS...     

模糊自适应Kalman滤波器在无人机导航中的应用

针对具有不确定动态模型参数的GPS／INS组合导航系统,基于传统Kalman滤波器之上,介绍了一种模糊自适应Kalman滤波器,讨论了GPS／INS组合系统中模型参数不确定性的问题,给出了一种利用模糊自适应滤波方法进行数据融合的无人机定位误差修正方法;仿真结果表明,模糊自适应卡尔曼滤波器对非线性GPS／INS组合系统是很有效的,提高了定位精度。     

基于多传感信息融合的语义词袋SLAM优化算法

针对室外大范围场景移动机器人建图中,激光雷达里程计位姿计算不准确导致SLAM (simultaneous localization and mapping)算法精度下降的问题,提出一种基于多传感信息融合的SLAM语义词袋优化算法MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags)。采用视觉惯性系统引入激光雷达原始观测数据,并通过滑动窗口实现了IMU (inertia measurement unit)量测、视觉特征和激光点云特征的多源数据联合非线性优化;最后算法利用视觉与激光雷达的语义词袋互补特性进行闭环优化,进一步提升了多传感器融合SLAM系统的全局定位和建图精度。实验结果显示,相比于传统的紧耦合双目视觉惯性里程计和激光雷达里程计定位,MSW-SLAM算法能够有效探测轨迹中的闭环信息,并实现高精度的全局位姿图优化,闭环检测后的点云地图具有良好的分辨率和全局一致性。     

Wavelet Neural Network for Corrections Prediction in Single-Frequency GPS Users

Accurate and reliable position determination is a vital component in Global Positioning System (GPS). GPS positioning errors occur from the cumulative effects of receiver, satellite and atmosphere, and also due to the U.S. military intentionally such as Selective Availability (SA). In order to improve the accuracy of positions provided by GPS additional correction information may be used, such as Differential GPS (DGPS) or other sensors to enhance position reliability. The DGPS has the problem of slow updates. To overcome this limitation, DGPS corrections prediction has been proposed. The ability of Neural Networks (NNs) to discover nonlinear relationships in input data makes them ideal for modeling nonlinear dynamic systems. The Wavelet Neural Network (WNN) employing nonlinear wavelet basis function, which are localized in both the time and frequency space, has been developed as an alternative approach to nonlinear fitting problem. Particle Swarm Optimization (PSO), a global optimization method, is used to train the WNN. In this paper, a WNN trained by a PSO algorithm is proposed for DGPS corrections prediction in single-frequency GPS receivers. Experimental results show the feasibility and effectiveness of the proposed method. The results are analyzed and compared with WNN trained by Back Propagation (BP) algorithm. The experimental results show that WNN, trained by the PSO algorithm, is able to reduce RMS errors to less than 1 m with SA on and 0.6 m with SA off.     

Cooperative localization based on robust GPS and Radar fusion for multiple aerial vehicles

This paper presents a localization framework for multiple aerial vehicles (AVs) based on sensor fusion of global positioning system (GPS) and the identification friend-or-foe (IFF) Radar system. The IFF-Radar play two roles: firstly, it detects the angle and the range between two AVs, and thus the path between the two vehicles can be modeled as a curve. Secondly, it detects which AV helps form the above curve, and receives the corresponding GPS information from the friend vehicle. Through the cooperation of the multiple AVs, not only GPS can work well with fewer satellites due to the block of canyon environments, but also differential GPS (DGPS) performance can be achieved when the place is beyond the coverage of the DGPS. Theoretical analysis shows that two GPS satellites are sufficient to obtain the location information with the help of two or more friend vehicles, and the performance is as accurate as DGPS with the help of two or more friend vehicles. Simulations show that the proposed approach can achieve better localization by the cooperation among multiple aerial vehicles than single aerial vehicle.

     

基于回算机制的GPS/INS时间延迟处理方法

针对组合导航中由于GPS时延而导致的定位精度下降的问题,提出了一种适用于低成本制导炸弹的时延处理方法.该方法在GPS数据输出延时过程中,利用预设存储器存储数据,基于回算机制完成GPS信息更新时刻的数据融合、GPS数据输出时刻的导航输出,减小时间不同步对组合导航数据融合的影响.该回算机制可控制计算量,不增加程序复杂性,适用于GPS数据丢失或异常等多种情况.针对回算机制提出了一种工程实践中的计算优化算法,在回算时取消卡尔曼滤波计算中的时间更新环节.该计算优化可节约回算过程的计算时间,避免整体数据延迟,同时不影响导航定位精度,可满足短时间内的低成本组合导航系统要求.靶试结果验证,时延处理方法及计算优化算法适用于低成本制导炸弹,具有一定的工程实用性.     

Expert system for an INS/DGPS integrated navigator installed in a Bell 206 helicopter

This paper presents a novel real-time application of fuzzy logic for an integrated navigation avionics suite. A knowledge-based system, which uses a fuzzy rule-base for a real-time INS/DGPS integrated navigation system on-board a Bell 206 helicopter, has been designed and developed. This knowledge base is developed in such a way as to detect aircraft maneuvering and tune the integration algorithm (Kalman filter) of the INS/DGPS system accordingly. The signal processing method developed for the integration of INS and DGPS data provides accurate navigation even during dynamic maneuvering of the aircraft, while taking advantage of low-cost modular equipment rather than costly inertial navigation systems.     

微小型群机器人室外组合定位系统与方法

传统群机器人的室外定位主要依靠单一的GPS定位,精度较低,且在城市及野外复杂环境下容易受到障碍物的影响,导致其无法获得准确位置。利用差分GPS定位技术和RSSI无线定位技术组建了一种基于AVR单片机的控制平台,XBee为通讯网络的组合定位系统,显著提高了定位精度,消除了定位盲点,在复杂环境下也能得到较为准确的位置,且具有低功耗,低成本,高稳定性的特点,并在实验和仿真中得到了验证。     

GPS/INS组合导航数据同步时标的计算方法研究

以GPS/INS组合导航系统为应用背景,针对GPS数据和惯性数据中时间同步的问题,提出了基于横摆角速度匹配、实时计算GPS延迟的方法,用于修正惯性数据与GPS的同步时标,提高了组合导航和车辆状态检测的精度。实验结果表明,计算得到的GPS延迟能够起到较好的修正效果。该方法仅利用导航系统数据,无需汽车内部参数和采集信号,具有自主性,有利于应用于外置检测和导航系统。