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Advances in Vision-Based UAV Navigation: Innovations and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 378

Special Issue Editors

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: vision navigation

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Guest Editor
School of Aerospace, Northwestern Polytechnical University, Xi’an 710129, China
Interests: aviation/space vehicle photoelectric detection technology; intelligent perception and anti-interference technology
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: intelligent image processing; machine learning and artificial intelligence
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Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have seen remarkable growth in recent years, driven by advances in sensor technology, computing power, and artificial intelligence. Vision-based navigation, which leverages cameras and computer vision, is a key enabling technology for autonomous UAVs. This Special Issue aims to capture the latest innovations and applications in vision-based UAV navigation. It will cover a broad range of topics, from fundamental research to practical implementations, providing a comprehensive overview of the current state of the art and future directions in this dynamic field. By bringing together leading researchers and practitioners, this Special Issue will serve as a valuable resource for advancing the capabilities and applications of UAVs.

This Special Issue focuses on the recent advancements and emerging trends in vision-based navigation for unmanned aerial vehicles (UAVs). We seek original research articles, review papers, and case studies that highlight theoretical developments, novel algorithms, sensor fusion techniques, and practical implementations that enhance the performance, robustness, and autonomy of UAVs. Topics of interest include, but are not limited to, visual simultaneous localization and mapping (VSLAM), real-time obstacle detection and avoidance, machine learning for environmental perception, sensor integration, and energy-efficient navigation. We also encourage submissions that explore the application of vision-based navigation in diverse fields such as urban environments, agriculture, surveillance, and disaster response.

Dr. Bo Li
Dr. Shaoyi Li
Dr. Shun Zhang
Guest Editors

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Keywords

  • vision-based navigation
  • unmanned aerial vehicles (UAVs)
  • visual SLAM
  • obstacle detection and avoidance
  • machine learning
  • sensor fusion
  • real-time processing
  • energy-efficient navigation
  • urban environments
  • agricultural applications

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Published Papers (1 paper)

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Research

22 pages, 6674 KiB  
Article
PLY-SLAM: Semantic Visual SLAM Integrating Point–Line Features with YOLOv8-seg in Dynamic Scenes
by Huan Mao and Jingwen Luo
Sensors 2025, 25(12), 3597; https://doi.org/10.3390/s25123597 - 7 Jun 2025
Viewed by 55
Abstract
In dynamic and low texture environments, traditional point-feature-based visual SLAM (vSLAM) often faces the challenges of poor robustness and low localization accuracy. To this end, this paper proposes a semantic vSLAM approach that fuses point-line features with YOLOv8-seg. First, we designed a high-performance [...] Read more.
In dynamic and low texture environments, traditional point-feature-based visual SLAM (vSLAM) often faces the challenges of poor robustness and low localization accuracy. To this end, this paper proposes a semantic vSLAM approach that fuses point-line features with YOLOv8-seg. First, we designed a high-performance 3D line-segment extraction method that determines the number of points to be sampled for each line-segment in terms of the length of the 2D line-segments extracted from the image, and back-projects these sampled points combined with the depth image to obtain the 3D point set of the line-segments. On this basis, accurate 3D line-segment fitting is realized in combination with the RANSAC algorithm. Subsequently, we introduce Delaunay triangulation to construct the geometric relationships between map points, detect dynamic feature points by matching changes in the topological structure of feature points in adjacent frames, and combine them with the instance labels provided by the YOLOv8-seg to accurately remove dynamic feature points. Finally, a loop-closure detection mechanism that fuses point–line features with instance-level matching is designed to calculate a normalized similarity score by combining the positional similarity of the instances, the scale similarity, and the spatial consistency of the static instances. A series of simulations and experiments demonstrate the superior performance of our method. Full article
(This article belongs to the Special Issue Advances in Vision-Based UAV Navigation: Innovations and Applications)
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