GPS Fusion Research Articles

Residual-Based Fault Detection and Exclusion With Enhanced Localization Integrity

In this paper, we give an integrity framework for vehicle localization in urban environments based on fusion of GPS and dead reckoning. To guarantee localization integrity, a novel Kalman filter-based fault detection and exclusion method is presented to reject faulty measurements via a chi-square test with iterative residuals. For a tight upper-bound for position error, we introduce a protection level calculation method with minimum detectable bias and the integrity budget allocation among all failure modes have been improved to enhance the accuracy of positioning. The algorithm provides integrity assessment and reliable estimations of vehicle position information in an iterative manner. Numerical simulations and experiments show that the proposed method can effectively deal with and recover from abrupt faults with a low rate of false alarm and a tight integrity risk bound.

Design of a Lightweight Autonomous Robotic Phenotyping System for Turfgrass

High-throughput field inspection and phenotyping are among the most promising applications for agricultural robotics. We present an autonomous, lightweight robotic system for turfgrass phenotyping applications. The robot is built on two integrated bikebot platform with high-clearance and low soil compaction. To improve the motion control accuracy, an invariant extended Kalman filter is built with fusion of GPS and inertial measurement unit for outdoor localization and navigation. Multiple cameras are used for multi-modal imaging collections. Preliminary experiment results show that the localization accuracy is at the centimeter level and the path-following error is less than 0.5 m most time for various reference trajectories in the feld. The robot design and autonomous navigation demonstrates the feasibility for turfgrass inspection application.

Fuzzy Fusion of Stereo Vision, Odometer, and GPS for Tracking Land Vehicles

The incorporation of high precision vehicle positioning systems has been demanded by the autonomous electric vehicle (AEV) industry. For this reason, research on visual odometry (VO) and Artificial Intelligence (AI) to reduce positioning errors automatically has become essential in this field. In this work, a new method to reduce the error in the absolute location of AEV using fuzzy logic (FL) is presented. The cooperative data fusion of GPS, odometer, and stereo camera signals is then performed to improve the estimation of AEV localization. Although the most important challenge of this work focuses on the reduction in the odometry error in the vehicle, the defiance of synchrony and the information fusion of sources of different nature is solved. This research is integrated by three phases: data acquisition, data fusion, and statistical evaluation. The first one is data acquisition by using an odometer, a GPS, and a ZED camera in AVE’s trajectories. The second one is the data analysis and fuzzy fusion design using the MatLab 2019® fuzzy logic toolbox. The last is the statistical evaluation of the positioning error of the different sensors. According to the obtained results, the proposed model with the lowest error is that which uses all sensors as input (stereo camera, odometer, and GPS). It can be highlighted that the best proposed model manages to reduce the positioning mean absolute error (MAE) up to 25% with respect to the state of the art.

Investigation on the commonality and consistency among data fusion algorithms with unknown cross-covariances and an improved algorithm

The cross-covariances among local sensor estimates are usually unknown or can’t be accurately known in multi-sensor systems. The Covariance Intersection (CI), Convex Combination (CC), Largest Ellipsoid (LE) and Ellipsoidal Intersection (EI) algorithms have been developed for the estimate fusion with unknown cross-covariances. In this contribution, we reveal a strong commonality in principle among CI, CC, LE and EI algorithms after a transformation into a new Euclidean space, although each algorithm is designed based on different criteria. We also assess the consistencies of CC, LE and EI algorithms under different conditions which have been found significantly dependent on the correlation level among local estimates. All the CI, CC, LE and EI algorithms have the capability to enhance consistency or accuracy but at a cost of accuracy or consistency. Based on the commonality and consistency features among different algorithms, an improved algorithm is presented which can significantly improve the consistency performance at a very little expense of accuracy. The theoretical analysis and the fusion algorithm selection strategy are testified through simulated examples and the fusion of GPS and GLONASS horizontal position solutions.

Recovery and guidance technology of collision network based on multi-source information fusion

To solve the problem of low precision of pose estimation and poor anti-jamming in the recovery process of small Shipborne UAV, a multi-sensor navigation scheme was proposed, based on vision / IMU / GPS fusion. Firstly, an integrated navigation scheme of multi-sensor fusion in collision recovery guidance was proposed. GPS is used for long-distance guidance. Vision / IMU / GPS fusion guidance is used in approach phase. GPS and visual information are fused based on extended Kalman filter to obtaion position information. IMU and visual information are fused based on least square method to obtain the optimal attitude information. According to this method, the optimal pose information is obtained to improve the guidance accuracy and the success rate of net collision recovery.The simulation results show that the guidance deviation is reduced by 30% compared with the unfused data.

Measurement of Structural Dynamic Behavior Using the Sensor Fusion of GPS and IMU

Measurement of Structural Dynamic Behavior Using the Sensor Fusion of GPS and IMU

A new Wi-Fi/ GPS fusion method for robust positioning in urban environments

This paper presents a tracking framework for enhancing the positioning accuracy of a mobile device by fusing the positions provided by a GPS navigation system and those obtained using Wi-Fi signal strength measurements, in urban environments. To achieve an efficient fusion, a structure based on two particle filters and a Multiple Model (MM) approach is proposed. It fuses the information coming from these two independent technologies, to overcome their own drawbacks. Indeed, the Wi-Fi and GPS are viewed as two models, whose probabilities are calculated using a Transition Probability Matrix (TPM) and a Mixing Likelihood Function (MLF). These probabilities are then used to combine the mobile state estimates, provided by the two particle filters. Matched to the two models, these filters interact by exchanging a part of their particles. The proposed architecture is experimentally evaluated and compared with the pure Wi-Fi and GPS positioning systems and other fusion methods. The results indicate that the positioning errors of the proposed scheme are the lowest.

Integrated Orbit Determination of FengYun‐3C, BDS, and GPS Satellites

AbstractAiming at weather forecast and global climate research, the satellite FengYun‐3C (FY‐3C), as the new generation of China's weather satellites, was launched in 2013. It carries the Global Navigation Satellite Systems Occultation Sounder instrument, a BDS + GPS receiver, to fulfill the demand of orbit determination and occultation. In this contribution, we develop an integrated orbit determination of FY‐3C, BDS, and GPS satellites by joint processing of onboard FY‐3C and ground data during 2013–2017. Four processing schemes, BDS, GPS, BDS + GPS, and BDS (without Geostationary Earth Orbit [GEO]) + GPS, are designed to investigate the capability of BDS for the integrated precise orbit determination and the benefit of BDS and GPS fusion. Regional and global networks are also employed to analyze the influence of station distribution on the orbit determination. The results show that with a regional network, both BDS and GPS orbits present a significant improvement after including FY‐3C data. For the BDS + GPS one‐step solution, GPS orbits are improved by 20% compared with the two‐step solution, while the improvement can reach 42%, 27%, and 27% for BDS GEO, Inclined Geosynchronous Satellite Orbit, and Medium Earth Orbit, respectively. The precision improvement of FY‐3C is respectively 26%, 44%, and 30% in along, cross, and radial components. For a global network, the orbit precision of the BDS + GPS solution is improved by 4% for GPS and 5%, 16%, and 19% for BDS GEO, Inclined Geosynchronous Satellite Orbit, and Medium Earth Orbit, respectively. For FY‐3C, the orbit improvement can reach 7%. The validation with satellite laser ranging also demonstrates the benefit of the integrated orbit determination. Such a high‐quality orbit will potentially benefit meteorological studies and applications.

A Gaussian process regression model for walking speed estimation using a head-worn IMU.

Miniature inertial sensors mainly worn on waist, ankle and wrist have been widely used to measure walking speed of the individuals for lifestyle and/or health monitoring. Recent emergence of head-worn inertial sensors in the form of a smart eyewear (e.g. Recon Jet) or a smart ear-worn device (e.g. Sensixa e-AR) provides an opportunity to use these sensors for estimation of walking speed in real-world environment. This work studies the feasibility of using a head-worn inertial sensor for estimation of walking speed. A combination of time-domain and frequency-domain features of tri-axial acceleration norm signal were used in a Gaussian process regression model to estimate walking speed. An experimental evaluation was performed on 15 healthy subjects during free walking trials in an indoor environment. The results show that the proposed method can provide accuracies of better than around 10% for various walking speed regimes. Additionally, further evaluation of the model for long (15-minutes) outdoor walking trials reveals high correlation of the estimated walking speed values to the ones obtained from fusion of GPS with inertial sensors.

Empirical Survey of the Solar System Based on the Fusion of GPS and Image Processing

Empirical Survey of the Solar System Based on the Fusion of GPS and Image Processing

A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and VANET Based Inter-Vehicle Distance Measurement

Accurate and reliable vehicle localization is a key component of numerous automotive and Intelligent Transportation System (ITS) applications, including active vehicle safety systems, real time estimation of traffic conditions, and high occupancy tolling [1]. Various safety critical vehicle applications in particular, such as collision avoidance or mitigation, lane change management or emergency braking assistance systems, rely principally on the accurate and reliable knowledge of vehicles? positioning within given vicinity.

GPS/RTS data fusion to overcome signal deficiencies in certain bridge dynamic monitoring projects

Measurement of deflections of certain bridges is usually hampered by corruption of the GPS signal by multipath associated with passing vehicles, resulting to unrealistically large apparent displacements. Field data from the Gorgopotamos train bridge in Greece and systematic experiments revealed that such bias is due to superimposition of two major effects, (i) changes in the geometry of satellites because of partial masking of certain satellites by the passing vehicles (this effect can be faced with solutions excluding satellites that get temporarily blocked by passing vehicles) and (ii) dynamic multipath caused from reflection of satellite signals on the passing trains, a high frequency multipath effect, different from the static multipath. Dynamic multipath seems to have rather irregular amplitude, depending on the geometry of measured satellites, but a typical pattern, mainly consisting of a baseline offset, wide base peaks correlating with the sequence of main reflective surfaces of the vehicles passing next to the antenna. In cases of limited corruption of GPS signal by dynamic multipath, corresponding to scale distortion of the short-period component of the GPS waveforms, we propose an algorithm which permits to reconstruct the waveform of bridge deflections using a weak fusion of GPS and RTS data, based on the complementary characteristics of the two instruments. By application of the proposed algorithm we managed to extract semi-static and dynamic displacements and oscillation frequencies of a historical railway bridge under train loading by using noisy GPS and RTS recordings. The combination of GPS and RTS is possible because these two sensors can be fully collocated and have complementary characteristics, with RTS and GPS focusing on the long- and short-period characteristics of the displacement, respectively.

Incremental Fusion of Structure-from-Motion and GPS Using Constrained Bundle Adjustments

Two problems occur when bundle adjustment (BA) is applied on long image sequences: large calculation time and drift (or error accumulation). In recent work, the calculation time is reduced by local BAs applied in an incremental scheme. The drift may be reduced by fusion of GPS and Structure-from-Motion. An existing fusion method is BA minimizing a weighted sum of image and GPS errors. This paper introduces two constrained BAs for fusion which enforce an upper bound for the reprojection error. These BAs are alternatives to the existing fusion BA which does not guarantee a small reprojection error and requires a weight as input. Then, the three fusion BAs are integrated in an incremental Structure-from-Motion method based on local BA. Last, we will compare the fusion results on long monocular image sequences and low cost GPS.

Intelligent Vehicle Localization in Urban Environments Using EKF-based Visual Odometry and GPS Fusion

AbstractWe present a method for intelligent vehicle localization and 3D mapping in urban environments using integration of stereovision and Real-Time Kinematic GPS (RTK-GPS). In our approach, stereoscopic system is used to recover the scene geometry and to predict the camera motion. Accurate GPS positions are integrated to correct the vehicle positions and orientations using Extended Kalman Filter (EKF), and to rectify the global positions of reconstructed 3D landmarks based on the positions of reference stereo frames. The method was tested using data obtained by a real electrical GEM vehicle equipped with stereoscopic system and RTK-GPS in urban environments. The results show that the trajectory obtained by GPS/Vision integrated method can fit the ground truth better than the vision-only method, and can also avoid the position jumps aroused by GPS signal failures.

A SENSOR AND DATA FUSION ALGORITHM FOR ROAD GRADE ESTIMATION

Emerging driver assistance systems, such as look-ahead cruise Controllers for heavy duty vehicles, require high precision digital maps. This contribution presents a road grade estimation algorithm for fusion of GPS and vehicle real-time sensor data, with measurements from previous runs over the same road segment. The resulting road grade estimate is thus enhanced using measurements from additional traversals of known roads. Distributed data fusion is utilized to ensure that the storage requirement of known roads does not increase when additional measurements are processed. The implemented algorithm, which is based on extended Kalman filtering and smoothing, is described in detail. Experiments on a Scania test vehicle show the advantages and some of the challenges with the proposed approach.

Synergistic Fusion of GPS and Photogrammetrically Generated Elevation Models

Digital elevation models (DEMs) produced from photogrammetric data sources have long relied on the use of ground control points to give them scale and orientation. However, in areas such as coastlines, landslides, or glaciers, where identification of suitable natural features and pre-marking is difficult, the use of conventional ground control may be unfeasible. This paper reports on research that uses independently collected DEMs derived from kinematic GPS to orient surfaces produced by aerial photogrammetric methods, using a least-squares surface matching algorithm. During algorithm development, three stages of testing were carried out, using increasingly more complex datasets. Initially, simulated surfaces were used to validate the matching theory and program. Then, a DEM derived from conventional aerial photography was matched with a GPS model, highlighting the effectiveness of surface matching to recover systematic errors in datasets. Finally, surfaces derived from small format digital imagery were successfully fused with wireframe GPS surfaces, the high redundancy and automation potential creating an elegant and cheaper alternative to photocontrol.