Redirigiendo al acceso original de articulo en 15 segundos...
Inicio  /  Applied Sciences  /  Vol: 13 Par: 9 (2023)  /  Artículo
ARTÍCULO
TITULO

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

Sergei Chuprov    
Pavel Belyaev    
Ruslan Gataullin    
Leon Reznik    
Evgenii Neverov and Ilia Viksnin    

Resumen

In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV?s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV?s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region?s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

 Artículos similares

       
 
Haoyu Lin, Pengkun Quan, Zhuo Liang, Dongbo Wei and Shichun Di    
With the rise of electric vehicles, autonomous driving, and valet parking technologies, considerable research has been dedicated to automatic charging solutions. While the current focus lies on charging robot design and the visual positioning of charging... ver más
Revista: Applied Sciences

 
Wenhan Gao, Shanmin Zhou, Shuo Liu, Tao Wang, Bingbing Zhang, Tian Xia, Yong Cai and Jianxing Leng    
Sonar images have the characteristics of lower resolution and blurrier edges compared to optical images, which make the feature-matching method in underwater target tracking less robust. To solve this problem, we propose a particle filter (PF)-based unde... ver más

 
Yuexing Zhang, Yiping Li, Shuo Li, Junbao Zeng, Yiqun Wang and Shuxue Yan    
This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of... ver más

 
Xingxing Zhang, Changyu Hu, Hanhan Liu, Ronghua Du, Xiaofeng Zhou and Ling Wang    
The relative pose estimation of the space target is indispensable for on-orbit autonomous service missions. Line segment detection is an important step in pose estimation. The traditional line segment detectors show impressive performance under sufficien... ver más
Revista: Aerospace

 
Kathiravan Thangavel, Pablo Servidia, Roberto Sabatini, Pier Marzocca, Haytham Fayek, Santiago Husain Cerruti, Martin España and Dario Spiller    
Space-based Earth Observation (EO) systems have undergone a continuous evolution in the twenty-first century. With the help of space-based Maritime Domain Awareness (MDA), specially Automatic Identification Systems (AIS), their applicability across the w... ver más
Revista: Aerospace