scholarly journals A Scalable Framework for Map Matching Based Cooperative Localization

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6400
Author(s):  
Chizhao Yang ◽  
Jared Strader ◽  
Yu Gu
Keyword(s):  
Scalar Field ◽  
Satellite System ◽  
Cooperative Localization ◽  
Map Matching ◽  
Communication Constraints ◽  
Fusion Algorithm ◽  
Time Step ◽  
Different Types ◽  
Localization Performance ◽  
Global Navigation Satellite

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints.

Ocean Navigation Method Based on Multi-System and Multi-Source Data Fusion

2020 ◽  
pp. 1-16
Author(s):  
Xiang Wang ◽  
Jingxian Liu ◽  
Zhao Liu
Keyword(s):  
Data Fusion ◽  
Satellite System ◽  
Single Frequency ◽  
Fusion Algorithm ◽  
Ship Maneuvering ◽  
Source Data ◽  
Huge Impact ◽  
Potential Risks ◽  
Global Navigation Satellite ◽  
Navigation Method

Ship navigation requires accurate positioning, navigation and timing (PNT) data. PNT data from a single source has uncertainties and potential risks. Wrong PNT data has a huge impact on ship maneuvering, and at the same time, it may cause huge losses to national assets and national security. This chapter proposes a data fusion algorithm based on single-frequency global navigation satellite system (GNSS) and inertial navigation system (INS) to obtain PNT data, which can improve the availability, accuracy, reliability, continuity, and robustness. The experimental results show that the data fusion method combining median filtering and Kalman filtering can improve the system's ability to acquire PNT data. When blocking GNSS acquisition of PNT data, relying on INS, you can still obtain PNT data, which can make up for the ability to obtain PNT data from GNSS.

scholarly journals A Robust GNSS/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians

2022 ◽  
Vol 14 (2) ◽  
pp. 300
Author(s):  
Dongpeng Xie ◽  
Jinguang Jiang ◽  
Jiaji Wu ◽  
Peihui Yan ◽  
Yanan Tang ◽  
...  
Keyword(s):  
Low Cost ◽  
Single Point ◽  
Dead Reckoning ◽  
Satellite System ◽  
Integration Scheme ◽  
Fusion Algorithm ◽  
Zero Velocity ◽  
Single Antenna ◽  
Other Information ◽  
Global Navigation Satellite

Aiming at the problem of high-precision positioning of mass-pedestrians with low-cost sensors, a robust single-antenna Global Navigation Satellite System (GNSS)/Pedestrian Dead Reckoning (PDR) integration scheme is proposed with Gate Recurrent Unit (GRU)-based zero-velocity detector. Based on the foot-mounted pedestrian navigation system, the error state extended Kalman filter (EKF) framework is used to fuse GNSS position, zero-velocity state, barometer elevation, and other information. The main algorithms include improved carrier phase smoothing pseudo-range GNSS single-point positioning, GRU-based zero-velocity detection, and adaptive fusion algorithm of GNSS and PDR. Finally, the scheme was tested. The root mean square error (RMSE) of the horizontal error in the open and complex environments is lower than 1 m and 1.5 m respectively. In the indoor elevation experiment where the elevation difference of upstairs and downstairs exceeds 25 m, the elevation error is lower than 1 m. This result can provide technical reference for the accurate and continuous acquisition of public pedestrian location information.

scholarly journals Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2048
Author(s):  
Faan Wang ◽  
Weichao Zhuang ◽  
Guodong Yin ◽  
Shuaipeng Liu ◽  
Ying Liu ◽  
...  
Keyword(s):  
Satellite System ◽  
Superior Performance ◽  
Estimation Methods ◽  
Cooperative Localization ◽  
Absolute Position ◽  
Localization Accuracy ◽  
Vehicle Localization ◽  
Communication Devices ◽  
Positioning Method ◽  
Global Navigation Satellite

Precise localization is critical to safety for connected and automated vehicles (CAV). The global navigation satellite system is the most common vehicle positioning method and has been widely studied to improve localization accuracy. In addition to single-vehicle localization, some recently developed CAV applications require accurate measurement of the inter-vehicle distance (IVD). Thus, this paper proposes a cooperative localization framework that shares the absolute position or pseudorange by using V2X communication devices to estimate the IVD. Four IVD estimation methods are presented: Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD) and Double Differencing (DD). Several static and dynamic experiments are conducted to evaluate and compare their measurement accuracy. The results show that the proposed methods may have different performances under different conditions. The DD shows the superior performance among the four methods if the uncorrelated errors are small or negligible (static experiment or dynamic experiment with open-sky conditions). When multi-path errors emerge due to the blocked GPS signal, the PD method using the original pseudorange is more effective because the uncorrelated errors cannot be eliminated by the differential technique.

Precise Single-Frequency Positioning Using Low-Cost Receiver with the Aid of Lane-Level Map Matching for Land Vehicle Navigation

2020 ◽  
pp. 1-14
Author(s):  
Fei Liu ◽  
Yue Liu ◽  
Zhixi Nie ◽  
Yang Gao
Keyword(s):  
Low Cost ◽  
Digital Camera ◽  
Satellite System ◽  
Single Frequency ◽  
Map Matching ◽  
Digital Map ◽  
The Road ◽  
Ambiguity Fixing ◽  
Wide Range ◽  
Global Navigation Satellite

Precise positioning with low-cost single-frequency global navigation satellite system (GNSS) receivers has great potential in a wide range of applications because of its low price and improved accuracy. However, challenges remain in achieving reliable and accurate solutions using low-cost receivers. For instance, the successful ambiguity fixing rate could be low for real-time kinematic (RTK) while large errors may occur in precise point positioning (PPP) in some scenarios (e.g., trees along the road). To solve the problems, this paper proposes a method with the aid of additional lane-level digital map information to improve the accuracy and reliability of RTK and PPP solutions. In the method, a digital camera will be applied for lane recognition and the positioning solution from a low-cost receiver will be projected to the digital map lane link. With the projected point position as a constraint, the RTK ambiguity fixing rate and PPP performance can be enhanced. A field kinematic test was conducted to verify the improvement of the RTK and PPP solutions with the aid of map matching. The results show that the RTK ambiguity fixing rate can be increased and the PPP positioning error can be reduced by map matching.

scholarly journals Implication between Geophysical Events and the Variation of Seasonal Signal Determined in GNSS Position Time Series

2021 ◽  
Vol 13 (17) ◽  
pp. 3478
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Ahmed El-Mowafy ◽  
Michal Apollo ◽  
Zinovy Malkin ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite System ◽  
Phase Lag ◽  
Time Frequency ◽  
Richter Scale ◽  
Different Types ◽  
Before And After ◽  
Position Time Series ◽  
Global Navigation Satellite ◽  
Seasonal Signal

The seasonal signal determined by the Global Navigation Satellite System (GNSS), which is captured in the coordinate time series, exhibits annual and semi-annual periods. This signal is frequently modelled by two periodic signals with constant amplitude and phase-lag. The purpose of this study is to explore the implication of different types of geophysical events on the seasonal signal in three stages—in the time span that contains the geophysical events, before and after the geophysical event, but also the stationarity phenomena, which is analysed on approximately 200 reference stations from the EPN network since 1995. The novelty of the article is demonstrated by correlating three different types of geophysical events, such as earthquakes with a magnitude greater than 6° on the Richter scale, landslides, and volcanic activity, and analysing the variation in amplitude of the seasonal signal. The geophysical events situated within a radius of 30 km from the epicentre showed a higher seasonal value than when the timespan did not contain a geophysical event. The presence of flicker and random walk noise was computed using overlapping Hadamard variance (OHVAR) and the non-stationary behaviour of the time series of the CORS coordinates in the time frequency analysis was done using continuous wavelet transform (CWT).

scholarly journals NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7886
Author(s):  
Jieum Hyun ◽  
Hyun Myung
Keyword(s):  
Satellite System ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Interacting Multiple Model ◽  
Indoor Environments ◽  
Multiple Model ◽  
Localization Performance ◽  
The One ◽  
Global Navigation Satellite ◽  
Factor Estimation

Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

scholarly journals Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2166 ◽  
Author(s):  
Jeong Min Kang ◽  
Tae Sung Yoon ◽  
Euntai Kim ◽  
Jin Bae Park
Keyword(s):  
Ground Truth ◽  
Autonomous Driving ◽  
Satellite System ◽  
Map Matching ◽  
High Definition ◽  
Driver Assistance Systems ◽  
Vehicle Localization ◽  
Reference Map ◽  
Global Navigation Satellite ◽  
Link Information

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

scholarly journals A troposphere tomography method considering the weighting of input information

2017 ◽  
Vol 35 (6) ◽  
pp. 1327-1340 ◽  
Author(s):  
Qingzhi Zhao ◽  
Yibin Yao ◽  
Wanqiang Yao
Keyword(s):  
A Priori ◽  
Constraint Equation ◽  
Weather Conditions ◽  
Satellite System ◽  
Reference Station ◽  
Unit Weight ◽  
A Posteriori ◽  
Input Information ◽  
Different Types ◽  
Global Navigation Satellite

Abstract. Troposphere tomography measurement using a global navigation satellite system (GNSS) generally consists of several types of input information including the observation equation, horizontal constraint equation, vertical constraint equation, and a priori constraint equation. The reasonable weightings of input information are a prerequisite for ensuring the reliability of the adjustment of the parameters. This forms the focus of this research, which tries to determine the weightings, including the observations, for the same type of equation and the optimal weightings for different types of equations. The optimal weightings of the proposed method are realized on the basis of the stable equilibrium relationship between different types of a posteriori unit weight variances, which are capable of adaptively adjusting the weightings for different types of equations and enables the ratio between the two arbitrary a posteriori unit weight variances to tend to unity. A troposphere tomography experiment, which was used to consider these weightings, was implemented using global positioning system (GPS) data from the Hong Kong Satellite Positioning Reference Station Network (SatRef). Numerical results show the applicability and stability of the proposed method for GPS troposphere tomography assessment under different weather conditions. In addition, the root mean square (RMS) error in the water vapor density differences between tomography-radiosonde and tomography-ECMWF (European Centre for Medium-Range Weather Forecasts) are 0.91 and 1.63 g m−3, respectively, over a 21-day test.

Sign in / Sign up

Export Citation Format

Share Document