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Sensors
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Sensors and Algorithms for 3D Visual Analysis and SLAM
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Sensors and Algorithms for 3D Visual Analysis and SLAM

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 6137

Special Issue Editor

Dr. Andrew R. Willis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of North Carolina-Charlotte, Charlotte, NC 28223-0001, USA
Interests: computer vision; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The successful development of critical future technologies hinge on the analysis algorithms that take as input unorganized collections of sensed visual data, quickly and reliably process these measurements, and extract models that organize, integrate, and semantically explain the sensor data. This Special Issue invites submissions that describe novel academic research that investigates 3D sensing technologies and algorithms that extract 3D visual, geometric, or semantic information.

Dr. Andrew R. Willis
Guest Editor

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. Sensors 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 2600 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

  • 3D SLAM
  • distributed 3D SLAM
  • structure from motion (SfM)
  • 3D motion planning
  • 3D view planning
  • 3D object recognition
  • 3D scene recognition
  • 3D semantic completion

Published Papers (2 papers)

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Research

22 pages, 26831 KiB  
Article
UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications
by Jincheng Zhang, Artur Wolek and Andrew R. Willis
Sensors 2024, 24(7), 2204; https://doi.org/10.3390/s24072204 - 29 Mar 2024
Viewed by 379
Abstract
This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by [...] Read more.
This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results. Full article
(This article belongs to the Special Issue Sensors and Algorithms for 3D Visual Analysis and SLAM)
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16 pages, 746 KiB  
Article
An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments
by Qiuyu Zang, Kehua Zhang, Ling Wang and Lintong Wu
Sensors 2023, 23(3), 1359; https://doi.org/10.3390/s23031359 - 25 Jan 2023
Cited by 6 | Viewed by 5161
Abstract
Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address [...] Read more.
Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address the low accuracy of visual SLAM in outdoor dynamic environments. We propose an adaptive feature point selection system for outdoor dynamic environments. Initially, we utilize YOLOv5s with the attention mechanism to obtain a priori dynamic objects in the scene. Then, feature points are selected using an adaptive feature point selector based on the number of a priori dynamic objects and the percentage of a priori dynamic objects occupied in the frame. Finally, dynamic regions are determined using a geometric method based on Lucas-Kanade optical flow and the RANSAC algorithm. We evaluate the accuracy of our system using the KITTI dataset, comparing it to various dynamic feature point selection strategies and DynaSLAM. Experiments show that our proposed system demonstrates a reduction in both absolute trajectory error and relative trajectory error, with a maximum reduction of 39% and 30%, respectively, compared to other systems. Full article
(This article belongs to the Special Issue Sensors and Algorithms for 3D Visual Analysis and SLAM)
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