scholarly journals GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Xingxing Li ◽  
Huidan Wang ◽  
Shengyu Li ◽  
Shaoquan Feng ◽  
Xuanbin Wang ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Micro Electro Mechanical System ◽  
Global Navigation Satellite Systems ◽  
Signal Interference ◽  
Vehicle Navigation ◽  
Positioning Accuracy ◽  
Tightly Coupled ◽  
Mean Square Errors

AbstractAccurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

scholarly journals GPS AND GLONASS COMBINED STATIC PRECISE POINT POSITIONING (PPP)

2016 ◽  
Vol XLI-B1 ◽  
pp. 483-488 ◽  
Author(s):  
D. Pandey ◽  
R. Dwivedi ◽  
O. Dikshit ◽  
A. K. Singh
Keyword(s):  
Precise Point Positioning ◽  
Rapid Development ◽  
Three Dimensional ◽  
Global Navigation Satellite Systems ◽  
Open Pit ◽  
Positioning Accuracy ◽  
Dilution Of Precision ◽  
Satellite Visibility ◽  
Point Positioning ◽  
Gps Observations

With the rapid development of multi-constellation Global Navigation Satellite Systems (GNSSs), satellite navigation is undergoing drastic changes. Presently, more than 70 satellites are already available and nearly 120 more satellites will be available in the coming years after the achievement of complete constellation for all four systems- GPS, GLONASS, Galileo and BeiDou. The significant improvement in terms of satellite visibility, spatial geometry, dilution of precision and accuracy demands the utilization of combining multi-GNSS for Precise Point Positioning (PPP), especially in constrained environments. Currently, PPP is performed based on the processing of only GPS observations. Static and kinematic PPP solutions based on the processing of only GPS observations is limited by the satellite visibility, which is often insufficient for the mountainous and open pit mines areas. One of the easiest options available to enhance the positioning reliability is to integrate GPS and GLONASS observations. This research investigates the efficacy of combining GPS and GLONASS observations for achieving static PPP solution and its sensitivity to different processing methodology. Two static PPP solutions, namely standalone GPS and combined GPS-GLONASS solutions are compared. The datasets are processed using the open source GNSS processing environment <i>gLAB</i> 2.2.7 as well as <i>magicGNSS</i> software package. The results reveal that the addition of GLONASS observations improves the static positioning accuracy in comparison with the standalone GPS point positioning. Further, results show that there is an improvement in the three dimensional positioning accuracy. It is also shown that the addition of GLONASS constellation improves the total number of visible satellites by more than 60% which leads to the improvement of satellite geometry represented by Position Dilution of Precision (PDOP) by more than 30%.

scholarly journals Integration of Convolutional Neural Network and Error Correction for Indoor Positioning

2020 ◽  
Vol 9 (2) ◽  
pp. 74
Author(s):  
Eric Hsueh-Chan Lu ◽  
Jing-Mei Ciou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Error Correction ◽  
Spatial Information ◽  
Rapid Development ◽  
Indoor Positioning ◽  
Experimental Results ◽  
Global Navigation Satellite Systems ◽  
Signal Interference ◽  
Position Vector

With the rapid development of surveying and spatial information technologies, more and more attention has been given to positioning. In outdoor environments, people can easily obtain positioning services through global navigation satellite systems (GNSS). In indoor environments, the GNSS signal is often lost, while other positioning problems, such as dead reckoning and wireless signals, will face accumulated errors and signal interference. Therefore, this research uses images to realize a positioning service. The main concept of this work is to establish a model for an indoor field image and its coordinate information and to judge its position by image eigenvalue matching. Based on the architecture of PoseNet, the image is input into a 23-layer convolutional neural network according to various sizes to train end-to-end location identification tasks, and the three-dimensional position vector of the camera is regressed. The experimental data are taken from the underground parking lot and the Palace Museum. The preliminary experimental results show that this new method designed by us can effectively improve the accuracy of indoor positioning by about 20% to 30%. In addition, this paper also discusses other architectures, field sizes, camera parameters, and error corrections for this neural network system. The preliminary experimental results show that the angle error correction method designed by us can effectively improve positioning by about 20%.

scholarly journals Positioning Accuracy Comparison of GNSS Receivers Used for Mapping and Guidance of Agricultural Machines

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 924 ◽  
Author(s):  
Pietro Catania ◽  
Antonio Comparetti ◽  
Pierluigi Febo ◽  
Giuseppe Morello ◽  
Santo Orlando ◽  
...  
Keyword(s):  
Real Time ◽  
Precision Agriculture ◽  
Low Cost ◽  
Global Navigation Satellite Systems ◽  
Reference Trajectory ◽  
Positioning Error ◽  
Differential Correction ◽  
Positioning Accuracy ◽  
Real Time Kinematic ◽  
Gnss Receivers

Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth’s surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending on farm needs, is the first step for implementing precision agriculture. The aim of this work is to compare the positioning accuracy of four GNSS receivers, different for technical features and working modes: L1/L2 frequency survey-grade Real-Time Kinematic (RTK)-capable Stonex S7-G (S7); L1 frequency RTK-capable Stonex S5 (S5); L1 frequency Thales MobileMapper Pro (TMMP); low-cost L1 frequency Quanum GPS Logger V2 (QLV2). In order to evaluate the positioning accuracy of these receivers, i.e., the distance of the determined points from a reference trajectory, different tests, distinguished by the use or not of Real-Time Kinematic (RTK) differential correction data and/or an external antenna, were carried out. The results show that all satellite receivers tested carried out with the external antenna had an improvement in positioning accuracy. The Thales MobileMapper Pro satellite receiver showed the worst positioning accuracy. The low-cost Quanum GPS Logger V2 receiver surprisingly showed an average positioning error of only 0.550 m. The positioning accuracy of the above-mentioned receiver was slightly worse than that obtained using Stonex S7-G without the external antenna and differential correction (maximum positioning error 0.749 m). However, this accuracy was even better than that recorded using Stonex S5 without differential correction, both with and without the external antenna (average positioning error of 0.962 m and 1.368 m).

scholarly journals A Multi-Sensor Tight Fusion Method Designed for Vehicle Navigation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2551 ◽  
Author(s):  
Qifeng Lai ◽  
Hong Yuan ◽  
Dongyan Wei ◽  
Ningbo Wang ◽  
Zishen Li ◽  
...  
Keyword(s):  
Navigation System ◽  
Rapid Development ◽  
Satellite System ◽  
Urban Environments ◽  
Sensor Technology ◽  
Vehicle Navigation ◽  
Noise Compensation ◽  
Vehicle Navigation System ◽  
Tightly Coupled ◽  
Global Navigation Satellite

Using the Global Navigation Satellite System (GNSS), it is difficult to provide continuous and reliable position service for vehicle navigation in complex urban environments, due to the natural vulnerability of the GNSS signal. With the rapid development of the sensor technology and the reduction in their costs, the positioning performance of GNSS is expected to be significantly improved by fusing multi-sensors. In order to improve the continuity and reliability of the vehicle navigation system, we proposed a multi-sensor tight fusion (MTF) method by combining the inertial navigation system (INS), odometer, and barometric altimeter with the GNSS technique. Different fusion strategies were presented in the open-sky, insufficient satellite, and satellite outage environments to check the performance improvement of the proposed method. The simulation and real-device tests demonstrate that in the open-sky context, the error of sensors can be estimated correctly. This is useful for sensor noise compensation and position accuracy improvement, when GNSS is unavailable. In the insufficient satellite context (6 min), with the help of the barometric altimeter and a clock model, the accuracy of the method can be close to that in the open-sky context. In the satellite outage context, the error divergence of the MTF is obviously slower than the traditional GNSS/INS tightly coupled integration, as seen by odometer and barometric altimeter assisting.

scholarly journals Performance of Tightly Coupled Integration of GPS/BDS/MEMS-INS/Odometer for Real-Time High-Precision Vehicle Positioning in Urban Degraded and Denied Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Fei Liu ◽  
Houzeng Han ◽  
Xin Cheng ◽  
Binghao Li
Keyword(s):  
Success Rate ◽  
Real Time ◽  
High Precision ◽  
Ambiguity Resolution ◽  
Satellite System ◽  
Vehicle Navigation ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
High Rise ◽  
Tightly Coupled

Global Navigation Satellite System Real-Time Kinematic (GNSS-RTK) technology is widely used in vehicle navigation, but in complex environments such as urban high-rise street, wooded street, overpass, and tunnel, satellite signals are prone to attenuation or even unavailability. It brings great challenges to the continuous high-precision navigation. For this reason, a tightly coupled (TC) integration algorithm for GPS (Global Positioning System)/BDS (BeiDou Navigation Satellite System)/MEMS-INS (Micro-Electro-Mechanical System-Inertial Navigation System)/Odometer (GCIO) is proposed for vehicle navigation in complex urban environments. The accuracy improvement and ambiguity resolution (AR) performance are analysed in this research. First of all, the INS positioning error is constrained by fusion GPS/BDS (GC) and odometer; then, the predicted position information is used to aid GPS/BDS ambiguity resolution. In GNSS-denied environments, the odometer/INS integration is still carried out for continuous navigation. Real-time experiments are carried out in urban degraded and denied environments to validate the performance of the integrated system. In high-rise streets, the ambiguity fixing success rate of GCIO mode is 13.57% higher than that of GC mode. In the wooded street environment, the success rate has increased particularly significantly, by about 55 percent. The positioning accuracy analysis for open environment, high-rise street, wooded street, overpass, and tunnel is conducted. The experimental results show that in the above environment, the order of 0.1 m positioning accuracy can be achieved in the case of satellite outage for 1 minute, which can meet the positioning needs in most scenarios.

scholarly journals GPS AND GLONASS COMBINED STATIC PRECISE POINT POSITIONING (PPP)

2016 ◽  
Vol XLI-B1 ◽  
pp. 483-488
Author(s):  
D. Pandey ◽  
R. Dwivedi ◽  
O. Dikshit ◽  
A. K. Singh
Keyword(s):  
Precise Point Positioning ◽  
Rapid Development ◽  
Three Dimensional ◽  
Global Navigation Satellite Systems ◽  
Open Pit ◽  
Positioning Accuracy ◽  
Dilution Of Precision ◽  
Satellite Visibility ◽  
Point Positioning ◽  
Gps Observations

With the rapid development of multi-constellation Global Navigation Satellite Systems (GNSSs), satellite navigation is undergoing drastic changes. Presently, more than 70 satellites are already available and nearly 120 more satellites will be available in the coming years after the achievement of complete constellation for all four systems- GPS, GLONASS, Galileo and BeiDou. The significant improvement in terms of satellite visibility, spatial geometry, dilution of precision and accuracy demands the utilization of combining multi-GNSS for Precise Point Positioning (PPP), especially in constrained environments. Currently, PPP is performed based on the processing of only GPS observations. Static and kinematic PPP solutions based on the processing of only GPS observations is limited by the satellite visibility, which is often insufficient for the mountainous and open pit mines areas. One of the easiest options available to enhance the positioning reliability is to integrate GPS and GLONASS observations. This research investigates the efficacy of combining GPS and GLONASS observations for achieving static PPP solution and its sensitivity to different processing methodology. Two static PPP solutions, namely standalone GPS and combined GPS-GLONASS solutions are compared. The datasets are processed using the open source GNSS processing environment &lt;i&gt;gLAB&lt;/i&gt; 2.2.7 as well as &lt;i&gt;magicGNSS&lt;/i&gt; software package. The results reveal that the addition of GLONASS observations improves the static positioning accuracy in comparison with the standalone GPS point positioning. Further, results show that there is an improvement in the three dimensional positioning accuracy. It is also shown that the addition of GLONASS constellation improves the total number of visible satellites by more than 60% which leads to the improvement of satellite geometry represented by Position Dilution of Precision (PDOP) by more than 30%.

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

scholarly journals Robustness against Chirp Signal Interference of On-Board Vehicle Geodetic and Low-Cost GNSS Receivers

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5257
Author(s):  
Franc Dimc ◽  
Polona Pavlovčič-Prešeren ◽  
Matej Bažec
Keyword(s):  
Interference Mitigation ◽  
Low Cost ◽  
Density Ratio ◽  
Autonomous Driving ◽  
Signal Interference ◽  
Noise Power ◽  
Reference Base ◽  
Noise Power Spectral Density ◽  
Processing Abilities ◽  
Gnss Receivers

Robust autonomous driving, as long as it relies on satellite-based positioning, requires carrier-phase-based algorithms, among other types of data sources, to obtain precise and true positions, which is also primarily true for the use of GNSS geodetic receivers, but also increasingly true for mass-market devices. The experiment was conducted under line-of-sight conditions on a straight road during a period of no traffic. The receivers were positioned on the roof of a car travelling at low speed in the presence of a static jammer, while kinematic relative positioning was performed with the static reference base receiver. Interference mitigation techniques in the GNSS receivers used, which were unknown to the authors, were compared using (a) the observed carrier-to-noise power spectral density ratio as an indication of the receivers’ ability to improve signal quality, and (b) the post-processed position solutions based on RINEX-formatted data. The observed carrier-to-noise density generally exerts the expected dependencies and leaves space for comparisons of applied processing abilities in the receivers, while conclusions on the output data results comparison are limited due to the non-synchronized clocks of the receivers. According to our current and previous results, none of the GNSS receivers used in the experiments employs an effective type of complete mitigation technique adapted to the chirp jammer.

scholarly journals End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

2021 ◽  
Author(s):  
Baiyu Peng ◽  
Qi Sun ◽  
Shengbo Eben Li ◽  
Dongsuk Kum ◽  
Yuming Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
State Space ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Saliency Map ◽  
Hierarchical Architecture ◽  
Link Type ◽  
The Neural Network ◽  
End To End

AbstractRecent years have seen the rapid development of autonomous driving systems, which are typically designed in a hierarchical architecture or an end-to-end architecture. The hierarchical architecture is always complicated and hard to design, while the end-to-end architecture is more promising due to its simple structure. This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network, making it possible for the vehicle to learn end-to-end driving by itself. This paper firstly proposes an architecture for the end-to-end lane-keeping task. Unlike the traditional image-only state space, the presented state space is composed of both camera images and vehicle motion information. Then corresponding dueling neural network structure is introduced, which reduces the variance and improves sampling efficiency. Thirdly, the proposed method is applied to The Open Racing Car Simulator (TORCS) to demonstrate its great performance, where it surpasses human drivers. Finally, the saliency map of the neural network is visualized, which indicates the trained network drives by observing the lane lines. A video for the presented work is available online, https://youtu.be/76ciJmIHMD8 or https://v.youku.com/v_show/id_XNDM4ODc0MTM4NA==.html.

scholarly journals Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

2021 ◽  
Vol 13 (22) ◽  
pp. 4525
Author(s):  
Junjie Zhang ◽  
Kourosh Khoshelham ◽  
Amir Khodabandeh
Keyword(s):  
Autonomous Driving ◽  
Measurement Model ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
High Definition ◽  
Satellite Systems ◽  
The Road ◽  
Gnss Receivers ◽  
Vehicle Positioning ◽  
Global Navigation Satellite

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.

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