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

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.

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Performance Evaluation of Pedestrian Locations Based on Contemporary Smartphones

International Journal of Navigation and Observation ◽

10.1155/2017/6750346 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 2

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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Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

Sensors ◽

10.3390/s19224896 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4896 ◽

Cited By ~ 6

Author(s):

Mohamed Elsheikh ◽

Walid Abdelfatah ◽

Aboelmagd Nourledin ◽

Umar Iqbal ◽

Michael Korenberg

Keyword(s):

Real Time ◽

Low Cost ◽

Estimation Algorithm ◽

Satellite System ◽

Single Frequency ◽

The Road ◽

Land Vehicles ◽

On The Road ◽

Vehicle Automation ◽

Global Navigation Satellite

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

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Precise Single-Frequency Positioning Using Low-Cost Receiver with the Aid of Lane-Level Map Matching for Land Vehicle Navigation

Journal of Navigation ◽

10.1017/s0373463320000375 ◽

2020 ◽

pp. 1-14

Author(s):

Fei Liu ◽

Yue Liu ◽

Zhixi Nie ◽

Yang Gao

Keyword(s):

Low Cost ◽

Digital Camera ◽

Satellite System ◽

Single Frequency ◽

Map Matching ◽

Digital Map ◽

The Road ◽

Ambiguity Fixing ◽

Wide Range ◽

Global Navigation Satellite

Precise positioning with low-cost single-frequency global navigation satellite system (GNSS) receivers has great potential in a wide range of applications because of its low price and improved accuracy. However, challenges remain in achieving reliable and accurate solutions using low-cost receivers. For instance, the successful ambiguity fixing rate could be low for real-time kinematic (RTK) while large errors may occur in precise point positioning (PPP) in some scenarios (e.g., trees along the road). To solve the problems, this paper proposes a method with the aid of additional lane-level digital map information to improve the accuracy and reliability of RTK and PPP solutions. In the method, a digital camera will be applied for lane recognition and the positioning solution from a low-cost receiver will be projected to the digital map lane link. With the projected point position as a constraint, the RTK ambiguity fixing rate and PPP performance can be enhanced. A field kinematic test was conducted to verify the improvement of the RTK and PPP solutions with the aid of map matching. The results show that the RTK ambiguity fixing rate can be increased and the PPP positioning error can be reduced by map matching.

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Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

Sensors ◽

10.3390/s20113145 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3145 ◽

Cited By ~ 2

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.

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

Journal of Advanced Transportation ◽

10.1155/2018/6513970 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

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).

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Design of a Multiband Global Navigation Satellite System Radio Frequency Interference Monitoring Front-End with Synchronized Secondary Sensors

Sensors ◽

10.3390/s18082594 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2594

Author(s):

Aiden Morrison ◽

Nadezda Sokolova ◽

James Curran

Keyword(s):

Radio Frequency ◽

Global Navigation Satellite System ◽

Low Cost ◽

Satellite System ◽

Radio Frequency Interference ◽

Navigation Satellite ◽

Inertial Measurement ◽

Front End ◽

Global Navigation ◽

Global Navigation Satellite

This paper investigates the challenges of developing a multi-frequency radio frequency interference (RFI) monitoring and characterization system that is optimized for ease of deployment and operation as well as low per unit cost. To achieve this, we explore the design and development of a multiband global navigation satellite system (GNSS) front-end which is intrinsically capable of synchronizing side channel information from non-RF sensors, such as inertial measurement units and integrated power meters, to allow the simultaneous production of substantial amounts of sampled spectrum while also allowing low-cost, real-time monitoring and logging of detected RFI events. While the inertial measurement unit and barometer are not used in the RFI investigation discussed, the design features that provide for their precise synchronization with the RF sample stream are presented as design elements worth consideration. The designed system, referred to as Four Independent Tuners with Data-packing (FITWD), was utilized in a data collection campaign over multiple European and Scandinavian countries in support of the determination of the relative occurrence rates of L1/E1 and L5/E5a interference events and intensities where it proved itself a successful alternative to larger and more expensive commercial solutions. The dual conclusions reached were that it was possible to develop a compact low-cost, multi-channel radio frequency (RF) front-end that implicitly supported external data source synchronization, and that such monitoring systems or similar capabilities integrated within receivers are likely to be needed in the future due to the increasing occurrence rates of GNSS RFI events.

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Kinematic PPP Using Mixed GPS/Glonass Single-Frequency Observations

Artificial Satellites ◽

10.2478/arsa-2019-0008 ◽

2019 ◽

Vol 54 (3) ◽

pp. 97-112

Author(s):

Mostafa Hamed ◽

Ashraf Abdallah ◽

Ashraf Farah

Keyword(s):

Low Cost ◽

Satellite System ◽

Single Frequency ◽

Future Research ◽

Frequency Data ◽

Dual Frequency ◽

Trajectory Data ◽

Average Improvement ◽

Kinematic Ppp ◽

Global Navigation Satellite

Abstract Nowadays, Precise Point Positioning (PPP) is a very popular technique for Global Navigation Satellite System (GNSS) positioning. The advantage of PPP is its low cost as well as no distance limitation when compared with the differential technique. Single-frequency receivers have the advantage of cost effectiveness when compared with the expensive dual-frequency receivers, but the ionosphere error makes a difficulty to be completely mitigated. This research aims to assess the effect of using observations from both GPS and GLONASS constellations in comparison with GPS only for kinematic purposes using single-frequency observations. Six days of the year 2018 with single-frequency data for the Ethiopian IGS station named “ADIS” were processed epoch by epoch for 24 hours once with GPS-only observations and another with GPS/GLONASS observations. In addition to “ADIS” station, a kinematic track in the New Aswan City, Aswan, Egypt, has been observed using Leica GS15, geodetic type, dual-frequency, GPS/GLONASS GNSS receiver and single-frequency data have been processed. Net_Diff software was used for processing all the data. The results have been compared with a reference solution. Adding GLONASS satellites significantly improved the satellite number and Position Dilution Of Precision (PDOP) value and accordingly improved the accuracy of positioning. In the case of “ADIS” data, the 3D Root Mean Square Error (RMSE) ranged between 0.273 and 0.816 m for GPS only and improved to a range from 0.256 to 0.550 m for GPS/GLONASS for the 6 processed days. An average improvement ratio of 24%, 29%, 30%, and 29% in the east, north, height, and 3D position components, respectively, was achieved. For the kinematic trajectory, the 3D position RMSE improved from 0.733 m for GPS only to 0.638 m for GPS/GLONASS. The improvement ratios were 7%, 5%, 28%, and 13% in the east, north, height, and 3D position components, respectively, for the kinematic trajectory data. This opens the way to add observations from the other two constellations (Galileo and BeiDou) for more accuracy in future research.

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

Advances in Early Childhood and K-12 Education - Robots in K-12 Education ◽

10.4018/978-1-4666-0182-6.ch014 ◽

2012 ◽

pp. 284-300 ◽

Cited By ~ 2

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.

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

Signals ◽

10.3390/signals1020007 ◽

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.

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