Precise vehicle ego-localization using feature matching of pavement images

Jiang, Zijun; Xu, Zhijie; Li, Yunchao; Min, Haigen; Zhou, Jingmei

doi:10.1108/jicv-12-2019-0015

Cited by 4 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These aspects integrate what is called Intelligent Transportation System (ITS), and possesses a long range of different applications like the usage of the GPS module embedded in the smartphones to estimate traffic conditions. Another important aspect of tracking systems that gained attention in the last years is regarding autonomous systems where various different approaches have been developed as the integration between GPS, Dead Reckoning and image localization to improve position [1] estimation, or a GPS tracking system integrated with a Lidar solution to avoid obstacles [2]. Critical activities such as rescuing and security benefit directly from well developed tracking systems.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Automotive Tracking System with Signal Lost Compensation

Lopes

Bueno

2023

Preprint

View full text Add to dashboard Cite

GPS tracking systems are wildly used in today's world, helping to improve efficiency and security in fleet management systems, or even supporting first generation of autonomous vehicles to deliver a more precise data to Driver-Assistance features. Many different approaches have been tested both for terrestrial and aerial vehicles, having the Kalman Filter as the main source of estimation correctness for GPS measurements. It is proposed integrating to a Linear Kalman Filter a Madgwick Filter to improve the position estimation together with correcting the filter in case of non-linearity in the route, using low-cost sensors: a GPS module and a combination of a MPU-6050 and a HMC5883L to build a Inertial Measurement Unit. The main goal is to test the efficiency of a Madgwick Filter fused with a Kalman Filter for position estimation and effectiveness of using the Inertial Measurement Unit acceleration data to compensate eventual GPS signal loss. The system was built upon a simulation followed by an analysis in real-world data through an Embedded System. First, a simulation was built upon trigonometric functions to provide position, velocity and acceleration, in order to validate the filter and the signal loss compensation. Subsequently, the filter is implemented in a Lilygo TTGO v1.1 prototype board embedded with a ESP32-D0WDQ6 microcontroller and a GPS module. The embedded system provided a soft real-time system.

show abstract

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Automotive Tracking System with Signal Lost Compensation

Lopes

Bueno

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, by solving the bi-level programming model [23][24][25][26][27], we can assist the government in determining the optimal DECA width. Uncertainties are not considered in the study [28][29][30][31][32][33][34][35].…”

Section: Resultsmentioning

confidence: 99%

A Bi-Level Programming Model for China’s Marine Domestic Emission Control Area Design

Tian

Yan

et al. 2022

Sustainability

View full text Add to dashboard Cite

Due to the adverse impact of seaborne sulfur emissions on coastal areas, the Ministry of Transport of the People’s Republic of China is planning to implement a 0.1% sulfur cap on bunker fuel in the domestic emission control area (DECA) on 1 January 2025. As the current DECA width is only 12 NM, ships can bypass the DECA to reduce the use of high-priced ultra-low sulfur fuel oil (ULSFO) and thus save on fuel costs. The purpose of this study is first to assess the effect of China’s 12-NM-wide DECA policy and then to assist the government in determining the optimal DECA width. We develop a bi-level programming model to capture the relationship between the government policy and ship operators’ operations. In the lower-level programming model, we capture ship operators’ decisions regarding their ships’ sailing routes and speeds while considering the time required for fuel switching, which aims to minimize the total fuel costs over a given voyage. The optimal solution to the lower-level programming model is then embedded in the upper-level programming model to determine the optimal DECA width for the government, with the aim of minimizing the impact of seaborne sulfur emissions on the coastal area environment. The final results, obtained from computational experiments, validate the idea that ships tend to bypass the 12-NM-wide DECA and reduce their sailing speeds inside the DECA to decrease their use of ULSFO. Therefore, we recommend that the government increase the current DECA width to at least 112 NM to prevent ships from bypassing it and to achieve the desired sulfur reduction target.

show abstract

“…Based on [135], image-based localization can be divided into three groups. Primarily, the video surveillance based-methods track the vehicles based on cameras mounted on road infrastructure, and they determine their position based on cameras calibration after detection.…”

Section: Image Matchingmentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

View full text Add to dashboard Cite

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

show abstract

Precise vehicle ego-localization using feature matching of pavement images

Cited by 4 publications

References 25 publications

WITHDRAWN: Automotive Tracking System with Signal Lost Compensation

WITHDRAWN: Automotive Tracking System with Signal Lost Compensation

A Bi-Level Programming Model for China’s Marine Domestic Emission Control Area Design

Localization and Mapping for Self-Driving Vehicles: A Survey

Contact Info

Product

Resources

About