COOPERATIVE LOCALISATION USING IMAGE SENSORS IN A DYNAMIC TRAFFIC SCENARIO

Abstract. Localisation is one of the key elements in navigation. Especially due to the development in automated driving, precise and reliable localisation becomes essential. In this paper, we report on different cooperation approaches in visual localisation with two vehicles driving in a convoy formation. Each vehicle is equipped with a multi-sensor platform consisting of front-facing stereo cameras and a global navigation satellite system (GNSS) receiver. In the first approach, the GNSS signals are used as excentric observations for the projection centres of the cameras in a bundle adjustment, whereas the second approach uses markers on the front vehicle as dynamic ground control points (GCPs). As the platforms are moving and data acquisition is not synchronised, we use time dependent platform poses. These time dependent poses are represented by trajectories consisting of multiple 6 Degree of Freedom (DoF) anchor points between which linear interpolation takes place. In order to investigate the developed approach experimentally, in particular the potential of dynamic GCPs, we captured data using two platforms driving on a public road at normal speed. As a baseline, we determine the localisation parameters of one platform using only data of that platform. We then compute a solution based on image and GNSS data from both platforms. In a third scenario, the front platform is used as a dynamic GCP which can be related to the trailing platform by markers observed in the images acquired by the latter. We show that both cooperative approaches lead to significant improvements in the precision of the poses of the anchor points after bundle adjustment compared to the baseline. The improvement achieved due to the inclusion of dynamic GCPs is somewhat smaller than the one due to relating the platforms by tie points. Finally, we show that for an individual vehicle, the use of dynamic GCPs can compensate for the lack of GNSS data.

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Study the Effect of Radio Interference of the EHV Transmission Line on GNSS Signals

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.805-806.851 ◽

2013 ◽

Vol 805-806 ◽

pp. 851-854

Author(s):

Zhi Ge Jia ◽

Zhao Sheng Nie ◽

Wei Wang ◽

Xiao Guan ◽

Di Jin Wang

Keyword(s):

Transmission Line ◽

Transmission Lines ◽

Internal Noise ◽

Field Testing ◽

Satellite System ◽

Field Analysis ◽

Radio Interference ◽

Gnss Receiver ◽

Global Navigation Satellite ◽

Gnss Data

This work describes the field testing process of Global Navigation Satellite System (GNSS) receiver under 220KV, 500KV UHV transmission line and standard calibration field. Analysis for GNSS data results shows that the radio interference generated by EHV transmission lines have no effect on GNSS receiver internal noise levels and valid GNSS observation rate. Within 50 meters of the EHV transmission lines, the multi-path effects (mp1 and mp2 value) significantly exceeded the normal range and becomes larger with the increase of the voltage .outside 50 meters of the EHV transmission line, the multi-path effects have almost no effect on the high-precision GNSS observations.

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Terrain changes from images acquired on opportunistic flights by SfM photogrammetry

The Cryosphere ◽

10.5194/tc-11-827-2017 ◽

2017 ◽

Vol 11 (2) ◽

pp. 827-840 ◽

Cited By ~ 12

Author(s):

Luc Girod ◽

Christopher Nuth ◽

Andreas Kääb ◽

Bernd Etzelmüller ◽

Jack Kohler

Keyword(s):

Low Cost ◽

Rapid Change ◽

Satellite System ◽

Light Transport ◽

Area Of Interest ◽

Accuracy And Precision ◽

Global Navigation Satellite ◽

The Cost ◽

Gnss Data

Abstract. Acquiring data to analyse change in topography is often a costly endeavour requiring either extensive, potentially risky, fieldwork and/or expensive equipment or commercial data. Bringing the cost down while keeping the precision and accuracy has been a focus in geoscience in recent years. Structure from motion (SfM) photogrammetric techniques are emerging as powerful tools for surveying, with modern algorithm and large computing power allowing for the production of accurate and detailed data from low-cost, informal surveys. The high spatial and temporal resolution permits the monitoring of geomorphological features undergoing relatively rapid change, such as glaciers, moraines, or landslides. We present a method that takes advantage of light-transport flights conducting other missions to opportunistically collect imagery for geomorphological analysis. We test and validate an approach in which we attach a consumer-grade camera and a simple code-based Global Navigation Satellite System (GNSS) receiver to a helicopter to collect data when the flight path covers an area of interest. Our method is based and builds upon Welty et al. (2013), showing the ability to link GNSS data to images without a complex physical or electronic link, even with imprecise camera clocks and irregular time lapses. As a proof of concept, we conducted two test surveys, in September 2014 and 2015, over the glacier Midtre Lovénbreen and its forefield, in northwestern Svalbard. We were able to derive elevation change estimates comparable to in situ mass balance stake measurements. The accuracy and precision of our DEMs allow detection and analysis of a number of processes in the proglacial area, including the presence of thermokarst and the evolution of water channels.

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Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

Remote Sensing ◽

10.3390/rs12030411 ◽

2020 ◽

Vol 12 (3) ◽

pp. 411 ◽

Cited By ~ 2

Author(s):

Sangeetha Shankar ◽

Michael Roth ◽

Lucas Andreas Schubert ◽

Judith Anne Verstegen

Keyword(s):

Laser Scanner ◽

Satellite System ◽

Sensor Data ◽

Measurement Unit ◽

Center Line ◽

Railway Tracks ◽

Combination Of Methods ◽

Global Navigation Satellite ◽

Inertial Measurements ◽

Gnss Data

Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.

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Integrated GNSS Attitude and Position Determination based on an Affine Constrained Model

Journal of Navigation ◽

10.1017/s0373463317000522 ◽

2017 ◽

Vol 71 (1) ◽

pp. 134-150

Author(s):

Haiying Liu ◽

Lei Xu ◽

Xiaolin Meng ◽

Xibei Chen ◽

Junyi Li

Keyword(s):

Attitude Determination ◽

Satellite System ◽

Determination Method ◽

Position Determination ◽

Static Data ◽

Constraint Information ◽

Constrained Model ◽

Global Navigation Satellite ◽

Constraint Model ◽

Gnss Data

Global Navigation Satellite System (GNSS) attitude determination and positioning play an important role in many navigation applications. However, the two GNSS-based problems are usually treated separately. This ignores the constraint information of the GNSS antenna array and the accuracy is limited. To improve the performance of navigation, an integrated attitude and position determination method based on an affine constraint model is presented. In the first part, the GNSS array model and affine constrained attitude determination method are compared with the unconstrained methods. Then the integrated attitude and position determination method is presented. The performance of the proposed method is tested with a series of static data and dynamic experimental GNSS data. The results show that the proposed method can improve the success rate of ambiguity resolution to further improve the accuracy of attitude determination and relative positioning compared to the unconstrained methods.

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Subsidence determination in the Central Valley, California

Reports on Geodesy and Geoinformatics ◽

10.2478/rgg-2018-0012 ◽

2018 ◽

Vol 106 (1) ◽

pp. 35-42 ◽

Cited By ~ 1

Author(s):

Marcelo Romero ◽

Mike Mustafa Berber

Keyword(s):

Central Valley ◽

Satellite System ◽

Geodetic Survey ◽

Reference Station ◽

National Geodetic Survey ◽

The North ◽

Elevation Changes ◽

System Data ◽

Global Navigation Satellite ◽

Gnss Data

Abstract Twenty four hour GNSS (Global Navigation Satellite System) data acquired monthly for 5 years from 8 CORS (Continuously Operating Reference Station) stations in Central Valley, California are processed and vertical velocities of the points are determined. To process GNSS data, online GNSS data processing service APPS (Automatic Precise Positioning Service) is used. GNSS data downloaded from NGS (National Geodetic Survey) CORS are analyzed and subsidence at these points is portrayed with graphics. It is revealed that elevation changes range from 5 mm uplift in the north to 163 mm subsidence in the southern part of the valley.

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Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment

Remote Sensing ◽

10.3390/rs12101564 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1564 ◽

Cited By ~ 6

Author(s):

Kai-Wei Chiang ◽

Guang-Je Tsai ◽

Yu-Hua Li ◽

You Li ◽

Naser El-Sheimy

Keyword(s):

Urban Areas ◽

Satellite System ◽

Multisensor Fusion ◽

Automated Driving ◽

Integrity Assessment ◽

Localization And Mapping ◽

Self Driving Cars ◽

Global Navigation Satellite ◽

3D Lidar ◽

Ins Aiding

Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.

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An improved pixel-based water vapor tomography model

Annales Geophysicae ◽

10.5194/angeo-37-89-2019 ◽

2019 ◽

Vol 37 (1) ◽

pp. 89-100

Author(s):

Yibin Yao ◽

Linyang Xin ◽

Qingzhi Zhao

Keyword(s):

Water Vapor ◽

Three Dimensional ◽

Satellite System ◽

Reference Station ◽

Weather Forecasts ◽

Satellite Positioning ◽

Optimal Polynomial ◽

Improved Model ◽

Global Navigation Satellite ◽

Gnss Data

Abstract. As an innovative use of Global Navigation Satellite System (GNSS), the GNSS water vapor tomography technique shows great potential in monitoring three-dimensional water vapor variation. Most of the previous studies employ the pixel-based method, i.e., dividing the troposphere space into finite voxels and considering water vapor in each voxel as constant. However, this method cannot reflect the variations in voxels and breaks the continuity of the troposphere. Moreover, in the pixel-based method, each voxel needs a parameter to represent the water vapor density, which means that huge numbers of parameters are needed to represent the water vapor field when the interested area is large and/or the expected resolution is high. In order to overcome the abovementioned problems, in this study, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) by adaptive training for water vapor retrieval. Tomography experiments were carried out using the GNSS data collected from the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 25 March to 25 April 2014 under different scenarios. The tomographic results are compared to the ECMWF data and validated by the radiosonde. Results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.

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RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing

Agriculture ◽

10.3390/agriculture10070276 ◽

2020 ◽

Vol 10 (7) ◽

pp. 276 ◽

Cited By ~ 1

Author(s):

Sergio Cubero ◽

Ester Marco-Noales ◽

Nuria Aleixos ◽

Silvia Barbé ◽

Jose Blasco

Keyword(s):

Field Tests ◽

Satellite System ◽

Negative Influence ◽

Proximal Sensing ◽

Horticultural Crops ◽

Pests And Diseases ◽

Candidatus Liberibacter ◽

Global Navigation Satellite ◽

Gnss Data ◽

Field Robot

RobHortic is a remote-controlled field robot that has been developed for inspecting the presence of pests and diseases in horticultural crops using proximal sensing. The robot is equipped with colour, multispectral, and hyperspectral (400–1000 nm) cameras, located looking at the ground (towards the plants). To prevent the negative influence of direct sunlight, the scene was illuminated by four halogen lamps and protected from natural light using a tarp. A GNSS (Global Navigation Satellite System) was used to geolocate the images of the field. All sensors were connected to an on-board industrial computer. The software developed specifically for this application captured the signal from an encoder, which was connected to the motor, to synchronise the acquisition of the images with the advance of the robot. Upon receiving the signal, the cameras are triggered, and the captured images are stored along with the GNSS data. The robot has been developed and tested over three campaigns in carrot fields for the detection of plants infected with ‘Candidatus Liberibacter solanacearum’. The first two years were spent creating and tuning the robot and sensors, and data capture and geolocation were tested. In the third year, tests were carried out to detect asymptomatic infected plants. As a reference, plants were analysed by molecular analysis using a specific real-time Polymerase Chain Reaction (PCR), to determine the presence of the target bacterium and compare the results with the data obtained by the robot. Both laboratory and field tests were done. The highest match was obtained using Partial Least Squares-Discriminant Analysis PLS-DA, with a 66.4% detection rate for images obtained in the laboratory and 59.8% for images obtained in the field.

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Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

Remote Sensing ◽

10.3390/rs13194002 ◽

2021 ◽

Vol 13 (19) ◽

pp. 4002

Author(s):

Wen Zhang ◽

Xingliang Huo ◽

Yunbin Yuan ◽

Zishen Li ◽

Ningbo Wang

Keyword(s):

Mean Absolute Error ◽

Absolute Error ◽

Satellite System ◽

Electron Content ◽

Iri Model ◽

Ionospheric Electron Content ◽

The Mean ◽

Spatial Grid ◽

Global Navigation Satellite ◽

Gnss Data

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.

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A Newly Installed Seismic and Geodetic Observational System at Five Indonesian Volcanoes as Part of the SATREPS Project

Journal of Disaster Research ◽

10.20965/jdr.2019.p0006 ◽

2019 ◽

Vol 14 (1) ◽

pp. 6-17 ◽

Cited By ~ 1

Author(s):

Haruhisa Nakamichi ◽

Masato Iguchi ◽

Hetty Triastuty ◽

Hery Kuswandarto ◽

Iyan Mulyana ◽

...

Keyword(s):

Discharge Rate ◽

Local Area ◽

Satellite System ◽

Merapi Volcano ◽

Observational System ◽

Equivalent Rate ◽

Global Navigation Satellite ◽

Gnss Data ◽

Integrated Study ◽

Observation Period

“Integrated Study on Mitigation of Multimodal Disasters Caused by Ejection of Volcanic Products” Project was launched in March 2014 for the Galunggung, Guntur, Kelud, Merapi, and Semeru volcanoes. The objectives of the project include the development of an observational system for the prediction and real-time estimations of the discharge rate of volcanic products. Under the project, a team from the Sakurajima Volcano Research Center, Center for Volcanology and Geological Hazard Mitigation (CVGHM) and the Balai Penyelidikan dan Pengembangan Teknologi Kebencanaan Geologi (BPPTKG) initiated the installation of a digital seismic and global navigation satellite system (GNSS) observational network for the volcanoes in December 2014, and finished the installation in September 2015. The seismic and GNSS data are transmitted by wireless local area networks (WLANs) from the stations to an observatory at each target volcano. We introduced three Windows PC software for data analysis: the first for estimating the equivalent rate of ejected ash from a volcano, the second for continuous smoothing of tilt data and detecting inflation and deflation in the volcanic sources, and the third for continuously evaluating eruption urgency to predict the eruption time. The seismic and GNSS data were routinely transmitted to the Support Systems of Decision Making (SSDM) at CVGHM or BPPTKG. Data completeness varied from volcano to volcano; for example, the data acquired for Kelud volcano were relatively stable, while those for Merapi volcano were problematic, owing to a communication disruption in the WLAN. We obtained the seismic and GNSS data at the target volcanoes in the observation period since 2015 when they have been relatively quiet.

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