Visual Localization—Volume II

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Visualization and Computer Graphics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 11291

Special Issue Editor


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Guest Editor
Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes (LITIS), University of Rouen Normandy, 76800 Saint Etienne du Rouvray, France
Interests: computer vision; localization; artificial intelligence; calibration; autonomous vehicle; image processing; mobile robotics
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Special Issue Information

Dear Colleagues,

The tasks involved in autonomous navigation (UAVs, robots and autonomous vehicles) can be categorized into five major modules: perception, localization, mapping, planning and control.

The localization module aims to determine the vehicle's pose (3D location and orientation) and plays a critical role in autonomous navigation. Navigation safety and comfort are highly dependent on the accuracy and robustness of this module.

This localization can be absolute (GPS coordinates or metric coordinates in a known map) or relative (the localization of the vehicle with respect to its lane, with respect to its initial pose, etc.). Although there are systems dedicated to localization, such as GPS, the accuracy of localization and signal loss in difficult environments (indoor or urban environments) make them unsuitable for autonomous navigation.

When the localization module uses only one camera, it is referred to as visual localization. The latter is particularly important for improving the accuracy and robustness of localization in difficult environments.

This Special Issue of the Journal of Imaging aims to feature papers on recent advances in visual localization. All levels of localization are of interest for this Special Issue (i.e., visual odometry, structure from motion, simultaneous localization and mapping, and place recognition) for any method based on the use of at least one camera. We also encourage work based on multisensor fusion and on the use of emerging imaging techniques (plenoptic, event camera, etc.).

Prof. Dr. Rémi Boutteau
Guest Editor

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Keywords

  • visual localization
  • visual odometry
  • structure from motion (SfM)
  • simultaneous localization and mapping (SLAM)
  • bundle adjustment
  • place recognition
  • mapping
  • tracking
  • pose estimation
  • long-term visual localization
  • localization with emerging sensors (plenoptic camera and event camera)
  • object detection and localization
  • visual descriptors for efficient localization
  • sensor fusion for localization (camera/lidar, visual/inertial, etc.)
  • indoor localization
  • deep learning for visual localization
  • semantic visual localization

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Related Special Issue

Published Papers (3 papers)

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Research

25 pages, 10696 KiB  
Article
Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI
by Zhiyi Liu, Tingting Li, Tianyi Ren, Da Chen, Wenjing Li and Waishan Qiu
J. Imaging 2024, 10(5), 112; https://doi.org/10.3390/jimaging10050112 - 7 May 2024
Cited by 1 | Viewed by 2195
Abstract
A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to [...] Read more.
A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments. Full article
(This article belongs to the Special Issue Visual Localization—Volume II)
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21 pages, 8234 KiB  
Article
Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model
by Suhong Yoo and Namhoon Kim
J. Imaging 2023, 9(12), 279; https://doi.org/10.3390/jimaging9120279 - 14 Dec 2023
Cited by 1 | Viewed by 1998
Abstract
This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using [...] Read more.
This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m. Full article
(This article belongs to the Special Issue Visual Localization—Volume II)
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19 pages, 2421 KiB  
Article
Automated Vehicle Counting from Pre-Recorded Video Using You Only Look Once (YOLO) Object Detection Model
by Mishuk Majumder and Chester Wilmot
J. Imaging 2023, 9(7), 131; https://doi.org/10.3390/jimaging9070131 - 27 Jun 2023
Cited by 13 | Viewed by 6611
Abstract
Different techniques are being applied for automated vehicle counting from video footage, which is a significant subject of interest to many researchers. In this context, the You Only Look Once (YOLO) object detection model, which has been developed recently, has emerged as a [...] Read more.
Different techniques are being applied for automated vehicle counting from video footage, which is a significant subject of interest to many researchers. In this context, the You Only Look Once (YOLO) object detection model, which has been developed recently, has emerged as a promising tool. In terms of accuracy and flexible interval counting, the adequacy of existing research on employing the model for vehicle counting from video footage is unlikely sufficient. The present study endeavors to develop computer algorithms for automated traffic counting from pre-recorded videos using the YOLO model with flexible interval counting. The study involves the development of algorithms aimed at detecting, tracking, and counting vehicles from pre-recorded videos. The YOLO model was applied in TensorFlow API with the assistance of OpenCV. The developed algorithms implement the YOLO model for counting vehicles in two-way directions in an efficient way. The accuracy of the automated counting was evaluated compared to the manual counts, and was found to be about 90 percent. The accuracy comparison also shows that the error of automated counting consistently occurs due to undercounting from unsuitable videos. In addition, a benefit–cost (B/C) analysis shows that implementing the automated counting method returns 1.76 times the investment. Full article
(This article belongs to the Special Issue Visual Localization—Volume II)
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