GPS and Inertial Systems for High Precision Positioning on Motorways

2009 ◽  
Vol 62 (2) ◽  
pp. 351-363 ◽  
Author(s):  
J. E. Naranjo ◽  
F. Jiménez ◽  
F. Aparicio ◽  
J. Zato
Keyword(s):  
High Precision ◽  
Autonomous Vehicles ◽  
Information Source ◽  
Base Station ◽  
Precision Positioning ◽  
Differential Gps ◽  
Collision Warning ◽  
Positioning Systems ◽  
The Road ◽  
Lane Departure

The accurate location of a vehicle in the road is one of the most important challenges in the automotive field. The need for accurate positioning affects several in-vehicle systems like navigators, lane departure warning systems, collision warning and other related sectors such as digital cartography suppliers. The aim of this paper is to evaluate high precision positioning systems that are able to supply an on-the-centimetre accuracy source to develop on-the-lane positioning systems and to be used in future applications as an information source for autonomous vehicles that circulate at high speeds on public roads. In this paper we have performed some on-road experiments, testing several GPS-based systems: Autonomous GPS; RTK Differential GPS with a proprietary GPS base station; RTK Differential GPS connected to the public GPS base station network of the National Geographic Institute of Spain via vehicle-to-infrastructure GPRS communications; and GPS combination with inertial measurement systems (INS) for position accuracy maintenance in degraded satellite signal reception areas. In these tests we show the validity and the comparison of these positioning systems, allowing us to navigate, in some cases, on public roads at speeds near 120 km/h and up to 100 km from the start position without any significant accuracy reduction.

Issues in validation of pre-sliding friction models for ultra-high precision positioning

2018 ◽  
Vol 233 (3) ◽  
pp. 997-1006 ◽  
Author(s):  
Ervin Kamenar ◽  
Saša Zelenika
Keyword(s):  
High Precision ◽  
Sliding Friction ◽  
Hysteretic Behavior ◽  
Slip Model ◽  
Precision Positioning ◽  
Positioning Systems ◽  
The Real ◽  
Sliding Motion ◽  
Real Behavior ◽  
Friction Models

Friction is one of the main disturbances in nanometric positioning. Recently, it was shown that ultra-high precision positioning typically happens in the pre-sliding motion regime where friction is characterized by an elasto-plastic nonlinear hysteretic behavior with a marked stochastic variability. With the aim of providing the tools for the development of robust control typologies for ultra-high precision mechatronics devices, different pre-sliding friction models are thus considered in this work. The most relevant ones are hence experimentally validated, as well as compared in terms of the complexity of identifying their characteristic parameters and of simulating the factual dynamic response. It is hence shown that the generalized Maxwell-slip model can account for all the important pre-sliding frictional effects in nanometric positioning applications. A thorough sensitivity analysis of the parameters of the generalized Maxwell-slip model model is therefore performed allowing to establish that three Maxwell-slip blocks are the minimum needed to approximate the behavior of the real precision positioning systems, six blocks allow representing excellently the real behavior, while the slower dynamics, which induces a difficult real-time implementation, with a very limited gain in terms of model accuracy, does not justify the usage of a larger number of elements.

Hardware Structures for High Precision Pneutronic Systems

2014 ◽  
Vol 658 ◽  
pp. 541-546 ◽  
Author(s):  
Mihai Avram ◽  
Victor Constantin ◽  
Constantin Bucşan ◽  
Daniel Besnea ◽  
Alina Spanu
Keyword(s):  
High Precision ◽  
Weight Ratio ◽  
Nonlinear Behavior ◽  
Compressed Air ◽  
Power Weight ◽  
Precision Positioning ◽  
Positioning Accuracy ◽  
Large Power ◽  
Pneumatic Actuators ◽  
Positioning Systems

Pneutronic systems come with a series of advantages that are natural to working with compressed air, such as the large power/weight ratio of pneumatic actuators, easy and affordable installation and maintenance as well as being clean working systems. However, due to working with compressed air, there are a series of issues, such as static and transient nonlinear behavior, mostly due to the high compressibility of air. Thus, the behavior of such systems is hard to control, especially in terms of precision positioning. The paper deals with proposing three hardware configurations of pneutronic positioning systems in order to assure the imposed positioning accuracy in the presence of disturbances and the preservation in time of the obtained position.

scholarly journals A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

2021 ◽  
Vol 14 (1) ◽  
pp. 27
Author(s):  
Changqiang Wang ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yushi Hao ◽  
Zhengxu Shi ◽  
...  
Keyword(s):  
High Precision ◽  
Navigation System ◽  
Autonomous Vehicles ◽  
Outdoor Environment ◽  
Precision Positioning ◽  
Outdoor Environments ◽  
Tightly Coupled ◽  
Seamless Positioning ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

scholarly journals POINT CLOUD TRANSFORMATION USING SENSOR CALIBRATION INFORMATION FOR MAP DATA ADJUSTMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 521-525
Author(s):  
J. Lee ◽  
J. Bae ◽  
Y. Choi ◽  
I. Park ◽  
S. Hong ◽  
...  
Keyword(s):  
High Precision ◽  
Reference Point ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
High Performance ◽  
Location Information ◽  
Delivery Vehicle ◽  
The Road ◽  
Road Sign ◽  
Low Performance

Abstract. In order to operate autonomous vehicles and unmanned delivery vehicle, it is important to accurately acquire location of the device itself. However, since these devices are mainly operated in urban areas, there is a limit in obtaining location information based on GNSS. Therefore, it is necessary to utilize a method of calibrating its own location information by measuring the reference point provided by the existing high-precision map of the region. Point cloud based multi-dimensional high-precision maps are acquired in advance using high-performance LiDAR and GNSS devices for infrastructure such as roads, and provide a reference point for autonomous driving or map updating. Since such high-performance surveying equipment requires high cost, it is difficult to attach to autonomous vehicles or unmanned vehicle for commercialization. Therefore, autonomous vehicles or unmanned delivery vehicle are operated with relatively low performance LiDAR and GNSS, so it is often impossible to accurately measure the reference point, which directly leads to a decrease in the accuracy of the location information of the device. To compensate for this, this study proposes a point interpolation method to extract GCP information from sparse point cloud maps acquired with low performance LiDAR. The proposed method uses calibration parameters between point data and the image data acquired from the device. In general, images provide higher resolution than point clouds, even when using low-end cameras, so that the position of point coordinates relative to a reference point can be measured relatively accurately from the image and projection data of the point cloud. The data acquisition vehicle is an MMS vehicle that provides a panoramic image using four DSLRs and a point cloud with Velodyne VLP 16. The researchers first conducted a reference point survey on features such as road signs. The panorama image including the road sign was transformed into a bird eye’s view, and point projection was performed on the bird eye’s view image. The reference point coordinates, which were not acquired by the point cloud, were obtained from the shape of the road sign in the bird eye’s view image, and the accuracy was compared with the measured data.

scholarly journals Possibilities of high precision GPS data in autonomous driving

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Márton Pál ◽  
Fanni Vörös ◽  
István Elek ◽  
Béla Kovács
Keyword(s):  
Real Time ◽  
High Precision ◽  
Autonomous Driving ◽  
Positioning Systems ◽  
The Road ◽  
Sample Area ◽  
Virtual Station ◽  
Position Data ◽  
On The Road ◽  
Self Driving Cars

<p><strong>Abstract.</strong> A self-driving car is a vehicle that is able to perceive its surroundings and navigate in it without human action. Radar sensors, lasers, computer vision and GPS technologies help it to drive individually (Figure 1). They interpret the sensed information to calculate routes and navigate between obstacles and traffic elements.</p><p>Sufficiently accurate navigation and information about the current position of the vehicle are indispensable for transport. These expectations are fulfilled in the case of a human driver: the knowledge on traffic rules and signs make possible to navigate through even difficult situations. Self-driving systems substitute humans by monitoring and evaluating the surrounding environment and its objects without the background information of the driver. This analysing process is vulnerable. Sudden or unexpected situations may occur but high precision navigation and background GPS databases can complement sensor-detected data.</p><p>The assistance of global navigation has been used in cars for decades. Drivers can easily plan their routes and reach their destination by using car GPS units. However, these devices do not provide accurate positioning: there may be a difference of several metres from the real location. Self-driving cars also use navigation to complement sensor data. Although there are already autonomous system tests on motorways and countryside roads, in densely built-in areas this technology faces complications due to accuracy problems. The dilution of precision (DOP) values can be extremely high in larger settlements because high buildings may hide southern sky (where satellite signs are sensed from on our latitude).</p><p>We can achieve centimetre-level accuracy (if the conditions are ideal) with geodesic RTK (real-time kinematic) GPS systems. This high-precision position data is derived from satellite-based positioning systems. Measurements of the phase of the signal’s carrier wave are real-time corrected by a single reference or an interpolated virtual station.</p><p>In this research we use RTK GPS technology in order to work out a spatial database. These measurements can also be less precise in dense cities, but there is time during fieldwork to try to eliminate inaccuracy. We have chosen a sample area in the inner city of Budapest, Hungary where we located all traffic signs, pedestrian crossings and other important elements. As self-driving cars need precise position data of these terrain objects, we have tried to work with a maximum error of a few decimetres.</p><p>We have examined online map providers if they have feasible data structure and some base data. The implemented structure is similar to OpenStreetMap DB, in which there are already some traffic lights in important crossings. With this preliminary test database, we would like to filter out dangerous situations. If the camera of the car does not see a traffic sign because of a tree or a truck, information about it will be available from the database. If a pedestrian crossing is hardly visible and the sensor does not recognize it, the background GIS data will warn the car that there may be inattentive people on the road.</p><p>A test application has also been developed (Figure 2.), in which our Postgres/Postgis database records have been inserted. In the next phase of the project we try to test our database in the traffic. We plan to drive through the sample area and observe the GPS accuracy in the recognition of the located signs.</p><p>This research aims to achieve higher safety in the field of autonomous driving. By having a refreshable cartographic GIS database in the memory of a self-driving car, there is a smaller chance of risking human life. However, the maintenance demands a high amount of work. Because of this we should concentrate only on the most important signs. Even the cars can be able to supervise the content of the database if there is a large number of them on the road. The frequent production and analysis of point clouds is also an option to get nearer to safe automatized traffic.</p>

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