scholarly journals Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3145 ◽  
Author(s):  
Miguel Ángel de Miguel ◽  
Fernando García ◽  
José María Armingol
Keyword(s):  
Monte Carlo ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Autonomous Vehicle ◽  
Satellite System ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
Global Navigation Satellite ◽  
The University ◽  
Gnss Data

This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.

scholarly journals Mono-Vision Based Lateral Localization System of Low-Cost Autonomous Vehicles Using Deep Learning Curb Detection

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 57
Author(s):  
Junwei Yu ◽  
Zhuoping Yu
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Satellite System ◽  
Localization Accuracy ◽  
Localization System ◽  
The Road ◽  
Cluttered Environment ◽  
Lateral Distance ◽  
Global Navigation Satellite ◽  
Lateral Localization

The localization system of low-cost autonomous vehicles such as autonomous sweeper requires a highly lateral localization accuracy as the vehicle needs to keep a near lateral-distance between the side brush system and the road curb. Existing methods usually rely on a global navigation satellite system that often loses signal in a cluttered environment such as sweeping streets between high buildings and trees. In a GPS-denied environment, map-based methods are often used such as visual and LiDAR odometry systems. Apart from heavy computation costs from feature extractions, they are too expensive to meet the low-price market of the low-cost autonomous vehicles. To address these issues, we propose a mono-vision based lateral localization system of an autonomous sweeper. Our system relies on a fish-eye camera and precisely detects road curbs with a deep curb detection network. Curbs locations are then referred to as straightforward marks to control the lateral motion of the vehicle. With our self-recorded dataset, our curb detection network achieves 93% pixel-level precision. In addition, experiments are performed with an intelligent sweeper to prove the accuracy and robustness of our proposed approach. Results demonstrate that the average lateral distance error and the maximum invalid rate are within 0.035 m and 9.2%, respectively.

Autonomous vehicle self-localization in urban environments based on 3D curvature feature points – Monte Carlo localization

Robotica ◽  
2021 ◽  
pp. 1-17
Author(s):  
Qi Liu ◽  
Xiaoguang Di ◽  
Binfeng Xu
Keyword(s):  
Monte Carlo ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Mapping Method ◽  
Urban Environments ◽  
Feature Points ◽  
Localization Algorithm ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Monte Carlo Localization

Abstract This paper proposes a map-based localization system for autonomous vehicle self-localization in urban environments, which is composed of a pose graph mapping method and 3D curvature feature points – Monte Carlo Localization algorithm (3DCF-MCL). The advantage of 3DCF-MCL is that it combines the high accuracy of the 3D feature points registration and the robustness of particle filter. Experimental results show that 3DCF-MCL can provide an accurate localization for autonomous vehicles with the 3D point cloud map that generated by our mapping method. Compared with other map-based localization algorithms, it demonstrates that 3DCF-MCL outperforms them.

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

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.

Learning Geospatial Concepts as Part of a Non-Formal Education Robotics Experience

2012 ◽  
pp. 284-300 ◽  
Author(s):  
Viacheslav Adamchuk ◽  
Bradley S. Barker ◽  
Gwen Nugent ◽  
Neal Grandgenett ◽  
Megan Patent-Nygren ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Formal Education ◽  
Satellite System ◽  
Summer Camps ◽  
Assessment Instruments ◽  
Virtual Tools ◽  
Youth Attitudes ◽  
And Mathematics ◽  
Global Navigation Satellite ◽  
Technological World

In the increasingly modern and technological world, it has become common to use global navigation satellite system (GNSS), such as Global Positioning System (GPS), receivers, and Geographic Information Systems (GIS) in everyday life. GPS-equipped mobile devices and various Web services help users worldwide to determine their locations in real-time and to explore unfamiliar land areas using virtual tools. From the beginning, geospatial technologies have been driven by the need to make efficient use of natural resources. More recently, GPS-equipped autonomous vehicles and aircraft have been under development to facilitate technological processes, such as agricultural operations, transportation, or scouting, with limited or virtual human control. As outdoor robotics relies upon a number of principles related to science, technology, engineering, and mathematics (STEM), using such an instructional context for non-formal education has been promising. As a result, the Geospatial and Robotics Technologies for the 21st Century program discussed in this chapter integrates educational robotics and GPS/GIS technologies to provide educational experiences through summer camps, 4-H clubs, and afterschool programs. The project’s impact was assessed in terms of: a) youth learning of computer programming, mathematics, geospatial and engineering/robotics concepts as well as b) youth attitudes and motivation towards STEM-related disciplines. An increase in robotics, GPS, and GIS learning questionnaire scores and a stronger self-efficacy in relevant STEM areas have been found through a set of project-related assessment instruments.

scholarly journals Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 230 ◽  
Author(s):  
Luisa Martelloni ◽  
Marco Fontanelli ◽  
Stefano Pieri ◽  
Christian Frasconi ◽  
Lisa Caturegli ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Global Navigation Satellite Systems ◽  
Forward Speed ◽  
Satellite Systems ◽  
Different Shapes ◽  
Global Navigation Satellite ◽  
High Level ◽  
Short Time ◽  
Gnss Data ◽  
Collection And Management

Before the introduction of positioning technologies in agriculture practices such as global navigation satellite systems (GNSS), data collection and management were time-consuming and labor-intensive tasks. Today, due to the introduction of advanced technologies, precise information on the performance of agricultural machines, and smaller autonomous vehicles such as robot mowers, can be collected in a relatively short time. The aim of this work was to track the performance of a robot mower in various turfgrass areas of an equal number of square meters but with four different shapes by using real-time kinematic (RTK)-GNSS devices, and to easily extract data by a custom built software capable of calculating the distance travelled by the robot mower, the forward speed, the cutting area, and the number of intersections of the trajectories. These data were then analyzed in order to provide useful functioning information for manufacturers, entrepreneurs, and practitioners. The path planning of the robot mower was random and the turfgrass area for each of the four shapes was 135 m2 without obstacles. The distance travelled by the robot mower, the mean forward speed, and the intersections of the trajectories were affected by the interaction between the time of cutting and the shape of the turfgrass. For all the different shapes, the whole turfgrass area was completely cut after two hours of mowing. The cutting efficiency decreased by increasing the time, as a consequence of the increase in overlaps. After 75 minutes of cutting, the efficiency was about 35% in all the turfgrass areas shapes, thus indicating a high level of overlapping.

scholarly journals Terrain changes from images acquired on opportunistic flights by SfM photogrammetry

2017 ◽  
Vol 11 (2) ◽  
pp. 827-840 ◽  
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.

scholarly journals Investigating the Benefits of Vector-Based GNSS Receivers for Autonomous Vehicles under Challenging Navigation Environments

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 121-137
Author(s):  
Haidy Y. F. Elghamrawy ◽  
Mohamed Tamazin ◽  
Aboelmagd Noureldin
Keyword(s):  
Autonomous Vehicles ◽  
Tracking System ◽  
Satellite System ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Positioning Accuracy ◽  
Gps Receivers ◽  
Car Industry ◽  
Vector Tracking ◽  
Global Navigation Satellite

There is a growing demand for robust and accurate positioning information for various applications, including the self-driving car industry. Such applications rely mainly on the Global Navigation Satellite System (GNSS), including the Global Positioning System (GPS). However, GPS positioning accuracy relies on several factors, such as satellite geometry, receiver architecture, and navigation environment, to name a few. In urban canyons in which there is a significant probability of signal blockage of one or more satellites and/or interference, the positioning accuracy of scalar-based GPS receivers drastically deteriorates. On the other hand, vector-based GPS receivers exhibit some immunity to momentary outages and interference. Therefore, it is becoming necessary to consider vector-based GPS receivers for several applications, especially safety-critical applications, including next-generation navigation technologies for autonomous vehicles. This paper investigates a vector-based receiver’s performance and compares it to its scalar counterpart in signal degraded conditions. The realistic simulation experiments in this paper are conducted on GPS L1 C/A signals generated using the SpirentTM simulation system to create a fully controlled environment to examine and validate the performance. The results show that the vector tracking system outperforms the scalar tracking in terms of position and velocity estimation accuracy in signal-degraded environments.

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

2020 ◽  
Vol 12 (3) ◽  
pp. 411 ◽  
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.

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

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.

scholarly journals Performance Evaluation of Pedestrian Locations Based on Contemporary Smartphones

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jalal Ibrahim Al-Azizi ◽  
Helmi Zulhaidi Mohd Shafri
Keyword(s):  
Performance Evaluation ◽  
Environmental Factors ◽  
Global Navigation Satellite System ◽  
Low Cost ◽  
Satellite System ◽  
Navigation Satellite ◽  
Localization Accuracy ◽  
Environment Type ◽  
Global Navigation ◽  
Global Navigation Satellite

Nowadays, a Global Navigation Satellite System (GNSS) unit is embedded in nearly every smartphone. This unit allows a smartphone to detect the user’s location and motion, and it makes functions, such as navigation, tracking, and compass applications, available to the user. Therefore, the GNSS unit has become one of the most important features in modern smartphones. However, because most smartphones incorporate relatively low-cost GNSS chips, their localization accuracy varies depending on the number of accessible GNSS satellites, and it is highly dependent on environmental factors that cause interference such as forests and buildings. This research evaluated the performance of the GNSS units inside two different models of smartphones in determining pedestrian locations in different environments. The results indicate that the overall performances of the two devices were related directly to the environment, type of smartphone/GNSS chipset, and the application used to collect the information.

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