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Electronics
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Advances in SLAM and Data Fusion for UAVs/Drones
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Advances in SLAM and Data Fusion for UAVs/Drones

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 8265

Special Issue Editors

Dr. Jacky C.K. Chow

E-Mail Website
Guest Editor
Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: photogrammetric computer vision; biomedical imaging; LiDAR; IMU; mobile robotics; simultaneous localization and mapping (SLAM); machine learning; sensor calibration; sensor fusion, and numerical optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Mozhdeh Shahbazi

E-Mail Website
Guest Editor
Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
Interests: vision-guided unmanned aerial systems; integration and calibration of ranging and imaging technologies; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ajeesh Kurian

E-Mail Website
Guest Editor
Leica Geosystems Inc., Calgary, AB T2E 8Z9, Canada
Interests: SLAM; computer vision; inertial navigation systems; signal processing; point cloud processing; LiDAR; calibration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) equipped with a variety of sensors can safely acquire real-time, high-resolution sensory data of the environment. This data can be applied in various fields, such as construction, agriculture, entertainment, and transportation. For many of these applications, the autonomy of the drone is of critical importance. UAVs need to be equipped with reliable localization, navigation, and exploration capabilities in order to make sense of complex environments by themselves with little/no interventions from operators. The users of UAV technologies are also inundated with overwhelming amounts of data (e.g., large volumes of imagery). Therefore, intelligent algorithms are needed to control the overflow of data by fusing and transforming disparate data into useful and concise information.

This Special Issue captures the state-of-the-art and emerging solutions for the localization and navigation of UAVs, as well as intelligent processing of data from the miniature sensors onboard.

You may choose our Joint Special Issue in Drones.

Dr. Jacky C.K. Chow
Dr. Mozhdeh Shahbazi
Dr. Ajeesh Kurian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicles
  • mapping and navigation
  • computer vision, photogrammetry, and remote sensing
  • control systems
  • signal processing
  • GNSS, IMU, UWB, BLE, Sonar, Radar, LiDAR and cameras
  • 2D/3D image and point cloud processing
  • image orientation
  • sensor/data fusion
  • machine learning and deep learning.

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Related Special Issue

Published Papers (2 papers)

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Research

13 pages, 2696 KiB  
Article
Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration
by Sameir A. Aziez, Nawar Al-Hemeary, Ahmed Hameed Reja, Tamás Zsedrovits and György Cserey
Electronics 2021, 10(13), 1513; https://doi.org/10.3390/electronics10131513 - 23 Jun 2021
Cited by 6 | Viewed by 1906
Abstract
The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction [...] Read more.
The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor. Full article
(This article belongs to the Special Issue Advances in SLAM and Data Fusion for UAVs/Drones)
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16 pages, 5585 KiB  
Article
A Simulated Annealing Algorithm and Grid Map-Based UAV Coverage Path Planning Method for 3D Reconstruction
by Sichen Xiao, Xiaojun Tan and Jinping Wang
Electronics 2021, 10(7), 853; https://doi.org/10.3390/electronics10070853 - 2 Apr 2021
Cited by 57 | Viewed by 5216
Abstract
With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of [...] Read more.
With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path. Full article
(This article belongs to the Special Issue Advances in SLAM and Data Fusion for UAVs/Drones)
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