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.

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

2013 ◽  
pp. 1368-1384
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 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 A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2491
Author(s):  
Mauro Tropea ◽  
Angelo Arieta ◽  
Floriano De Rango ◽  
Francesco Pupo
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Propagation Delay ◽  
Satellite System ◽  
Transportation Systems ◽  
Navigation Systems ◽  
Vehicle Positioning ◽  
Satellite Navigation Systems ◽  
Global Navigation Satellite

Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors.

scholarly journals Optimal Beacon Placement for Self-Localization Using Three Beacon Bearings

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
John McGuire ◽  
Yee Wei Law ◽  
Javaan Chahl ◽  
Kutluyıl Doğançay
Keyword(s):  
Autonomous Vehicles ◽  
Information Matrix ◽  
Satellite System ◽  
Optimal Placement ◽  
Angle Of Arrival ◽  
Placement Problem ◽  
Estimation Performance ◽  
Global Positioning ◽  
Heading Angle ◽  
Global Navigation Satellite

Autonomous vehicles need to localize themselves within the environment in order to effectively perform most tasks. In situations where a Global Navigation Satellite System such as the Global Positioning System cannot be used for localization, other methods are required. One self-localization method is to use signals transmitted by beacons at known locations to determine the relative distance and bearing of the vehicle from the beacons. Estimation performance is influenced by the beacon–vehicle geometry and the investigation into the optimal placement of beacons is of interest to maximize the estimation performance. In this article, a new solution to the optimal beacon placement problem for self-localization of a vehicle on a two-dimensional plane using angle-of-arrival measurements is proposed. The inclusion of heading angle in the estimation problem differentiates this work from angle-of-arrival target localization, making the optimization problem more difficult to solve. First, an expression of the determinant of the Fisher information matrix for an arbitrary number of beacons is provided. Then, a procedure for analytically determining the optimal angular separations for the case of three beacons is presented. The use of three beacons is motivated by practical considerations. Numerical simulations are used to demonstrate the optimality of the proposed method.

Integrity for autonomous vehicles and towards a novel alert limit determination method

2020 ◽  
pp. 095440702096576
Author(s):  
Qian Meng ◽  
Li-Ta Hsu
Keyword(s):  
Autonomous Vehicles ◽  
Performance Indicator ◽  
Kinematic Model ◽  
Satellite System ◽  
Civil Aviation ◽  
Determination Method ◽  
Integrity Assessment ◽  
Safety Critical ◽  
Bounding Box ◽  
Global Navigation Satellite

Integrity is one critical performance indicator for navigation in safety-critical applications such as autonomous vehicles. Alert limit is one of the representative parameters in integrity monitoring which defines the maximum tolerable positioning error for an operation to safely proceed. But the integrity requirements for global navigation satellite system (GNSS) assessment are quite different from those for other applications. For autonomous vehicles, a reasonable alert limit needs to ensure the vehicle security and take full advantage of the space between vehicle and lane as much as possible. Based on the analysis of integrity application differences from civil aviation to autonomous vehicles, an improved alert limit determination method is proposed in this paper. The kinematic model is firstly introduced into the online determination of alert limit. The integrity risk on two sides are allocated optimally respect to the road geometry and kinematic model. The fixed cuboid bounding box is replaced by a subversive fan-shaped bounding box which is more reasonable to cover the safety-critical areas. The discussion compared with the Ford model also verified the superiority of the proposed method. Finally the paper also gives the alert limits calculated based on the Chinese standards and hopefully it could provide some references for the navigation integrity assessment for autonomous vehicles.

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 Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Muhammed Tahsin Rahman ◽  
Tashfeen Karamat ◽  
Sidney Givigi ◽  
Aboelmagd Noureldin
Keyword(s):  
Autonomous Vehicles ◽  
High Performance ◽  
Inertial Sensors ◽  
Low Cost ◽  
Visual Odometry ◽  
Satellite System ◽  
Land Vehicles ◽  
Self Driving Cars ◽  
Global Navigation Satellite ◽  
Navigation Accuracy

For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs).

scholarly journals Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6805
Author(s):  
Jinhwan Jeon ◽  
Yoonjin Hwang ◽  
Yongseop Jeong ◽  
Sangdon Park ◽  
In So Kweon ◽  
...  
Keyword(s):  
Navigation System ◽  
Autonomous Vehicles ◽  
Computing Time ◽  
Dead Reckoning ◽  
Satellite System ◽  
Lane Detection ◽  
Urban Streets ◽  
Detection Model ◽  
Linearization Errors ◽  
Global Navigation Satellite

With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented.

scholarly journals Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

2021 ◽  
Vol 11 (3) ◽  
pp. 1270
Author(s):  
Uche Onyekpe ◽  
Vasile Palade ◽  
Stratis Kanarachos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Inertial Navigation ◽  
Satellite System ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Displacement Estimation ◽  
Global Navigation Satellite

An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation.

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.

Sign in / Sign up

Export Citation Format

Share Document