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: 30 April 2024 | Viewed by 6336

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

Manuscript Submission Information

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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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Published Papers (2 papers)

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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
Viewed by 1337
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 5 | Viewed by 4678
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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