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Drones Navigation and Orientation

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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (14 April 2025) | Viewed by 92875

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Special Issue Editors

Prof. Dr. Costas Armenakis

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Guest Editor

Geomatics Engineering, Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
Interests: photogrammetric engineering and remote sensing mapping; unmanned aerial mapping systems for geomatics; robotic mapping; algorithmic aspects for rapid processing of UAS data; data co-registration; image sequence for mapping and scene analysis; spatial awareness and intelligence; risk assessment and disaster management
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Dr. Ismael Colomina

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Guest Editor

GeoNumerics, 08860 Castelldefels, Barcelona, Spain
Interests: geomatics; navigation; geometric photogrammetry and remote sensing; sensor modelling; sensor integration; sensor orientation and calibration; multi-sensor integration for orientation and navigation; indoor/outdoor navigation; estimation methods; software systems for orientation and navigation; unmanned aerial systems; unmanned aerial systems for photogrammetry, remote sensing and mapping; combined terrestrial and aerial mobile mapping systems

Special Issue Information

Dear Colleagues,

The development of semi-autonomous and autonomous unmanned aerial systems, also known as drones, and their applications is experiencing continuous growth. Drones’ success has resulted in increased interest in other types of uncrewed vehicles, from submarines to planetary ground vehicles. Currently, the category ‘drone’ embraces all types of uncrewed vehicles, from underwater autonomous ones to planetary robots (excluding driverless cars).

Autonomous operation of drones in known and unknown environments requires continuous integration of navigation sensors data, perception of environmental elements with respect to space and time, understanding of the scene situation through data and information, anticipation of the next stages, and the ability to make quick knowledge-based decisions based on real-time position and navigation, scene sensing, recognition, understanding and visualization.

For most drone applications, the vehicle’s position and attitude—the drone’s orientation—is required to exploit the collected data. For all drone operations, knowing the drone orientation in real time—drone navigation—is required. Navigation is a fundamental function of drone guidance, control, and navigation (GNC) systems, often referred to as autopilots. In turn, GNC systems are a necessary subsystem of drones. Both drone orientation and navigation must meet certain performance specifications, often revolving around precision, accuracy, integrity, availability and continuity. The latter four are called the required navigation performance (RNP) parameters, and they define a family of navigation specifications.

The RNP family of specifications continues to grow alongside the advent of new drone applications. Where and when GNSS signals are available, RNPs often challenge GNSS performance. Otherwise, other (direct or indirect) motion-sensing technologies and methods must be used. Ultimately, multi-sensor systems and sensor fusion methods for orientation and navigation are at the very core of all types of drones, and this is an active field of research.

This Special Issue of Drones targets both real-time and post-mission determination of drone trajectories; including but not limited to uncrewed aerial systems and adds to the already interesting collection of related articles and Special Issues of the journal including the previous Special Issue devoted to UAS Navigation and Orientation.

We encourage the submission of quality, innovative articles covering overarching topics such as real-time robust navigation, high-accuracy post-mission orientation, small and large drones, and applications varying from autonomous land, underwater and aerial to planetary robotic platforms. Applications such drone-based photogrammetry and mapping, as well as developments pertaining to orientation and navigation performance when using the new GNSS signals and augmentation services, are also welcome.

We encourage submissions which present the most recent advancements in all aspects of Drone Navigation and Orientation, including (but not limited to):

Methods of extracting drone navigation and orientation user RNPs.
Direct, indirect and integrated sensor/platform positioning and orientation.
Vision-based navigation and orientation.
Single/multiple IMU–vision-based navigation and orientation.
Simultaneous Localization and Mapping (SLAM) methods.
Navigation and orientation using machine learning and deep learning.
Advanced techniques for motion sensor data fusion.
Computational aspects and incremental approaches.
Collaborative navigation of homogeneous and heterogeneous drone swarms.
Navigation and orientation in outdoors and indoors environments.
Autonomous navigation.
Obstacle detection and avoidance.
Precise Point Positioning-based (PPP) navigation.
Ultra-wideband (UWB) localization.
Optical flow-based vision-based navigation.
Trajectory tracking systems.
Autopilots and navigation: standard and advanced solutions for navigation integrity.
Path planning.

Beyond visual line of sight (BVLOS) navigation.

Prof. Dr. Costas Armenakis
Dr. Ismael Colomina
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. Drones is an international peer-reviewed open access monthly 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.

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Published Papers (10 papers)

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Research

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18 pages, 780 KiB

Open AccessArticle

Real-Time and Post-Mission Heading Alignment for Drone Navigation Based on Single-Antenna GNSS and MEMs-IMU Sensors

by João F. Contreras, Jitesh A. M. Vassaram, Marcos R. Fernandes and João B. R. do Val

Drones 2025, 9(3), 169; https://doi.org/10.3390/drones9030169 - 25 Feb 2025

Viewed by 556

Abstract

This paper presents a heading alignment procedure for drone navigation employing a single hover GNSS antenna combined with low-grade MEMs-IMU sensors. The design was motivated by the need for a drone-mounted differential interferometric SAR (DinSAR) application. Still, the methodology proposed here applies to any Unmanned Aerial Vehicle (UAV) application that requires high-precision navigation data for short-flight missions utilizing cost-effective MEMs sensors. The method proposed here involves a Bayesian parameter estimation based on a simultaneous cumulative Mahalanobis metric applied to the innovation process of Kalman-like filters, which are identical except for the initial heading guess. The procedure is then generalized to multidimensional parameters, thus called parametric alignment, referring to the fact that the strategy applies to alignment problems regarding some parameters, such as the heading initial value. The motivation for the multidimensional extension in the scenario is also presented. The method is highly applicable for cases where gyro-compassing is not available. It employs the most straightforward optimization techniques that can be implemented using a real-time parallelism scheme. Experimental results obtained from a real UAV mission demonstrate that the proposed method can provide initial heading alignment when the heading is not directly observable during takeoff, while numerical simulations are used to illustrate the extension to the multidimensional case. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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23 pages, 1756 KiB

Open AccessArticle

IVU-AutoNav: Integrated Visual and UWB Framework for Autonomous Navigation

by Shuhui Bu, Jie Zhang, Xiaohan Li, Kun Li and Boni Hu

Drones 2025, 9(3), 162; https://doi.org/10.3390/drones9030162 - 22 Feb 2025

Viewed by 709

Abstract

To address the inherent scale ambiguity and positioning drift in monocular visual Simultaneous Localization and Mapping (SLAM), this paper proposes a novel localization method that integrates monocular visual SLAM with Ultra-Wideband (UWB) ranging information. This method enables high-precision localization for unmanned aerial vehicles (UAVs) in complex environments without global navigation information. The proposed framework, IVU-AutoNav, relies solely on distance measurements between a fixed UWB anchor and the UAV’s UWB device. Initially, it jointly solves for the position of the UWB anchor and the scale factor of the SLAM system using the scale-ambiguous SLAM data and ranging information. Subsequently, a pose optimization equation is formulated, which integrates visual reprojection errors and ranging errors, to achieve precise localization with a metric scale. Furthermore, a global optimization process is applied to enhance the global consistency of the localization map and optimize the positions of the UWB anchors and scale factor. The proposed approach is validated through both simulation and experimental studies, demonstrating its effectiveness. Experimental results show a scale error of less than 1.8% and a root mean square error of 0.23 m, outperforming existing state-of-the-art visual SLAM systems. These findings underscore the potential and efficacy of the monocular visual-UWB coupled SLAM method in advancing UAV navigation and localization capabilities. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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15 pages, 6009 KiB

Open AccessArticle

Positioning Method for Unmanned Aerial Vehicle (UAV) Based on Airborne Two-Dimensional Laser Doppler Velocimeter: Experiment and Dead Reckoning

by Lanjian Chen, Chongbin Xi, Shilong Jin and Jian Zhou

Drones 2024, 8(12), 751; https://doi.org/10.3390/drones8120751 - 12 Dec 2024

Viewed by 1124

Abstract

In the autonomous navigation of drones, improving positioning accuracy is of significant importance to obtain highly accurate information on flight velocity. Traditional microwave and acoustic velocity measurement methods have the disadvantages of poor precision and susceptibility to interference. In this study, an unmanned aerial vehicle (UAV)-mounted two-dimensional laser Doppler velocimeter was developed and investigated, and a relevant drone flight navigation and positioning experiment was carried out. The UAV-mounted two-dimensional laser Doppler velocimeter (LDV) prototype developed in this study applies a scheme of dual-beam measurement light, sharing a focusing lens group. After process integration, the performance of the prototype was measured. It shows that a velocity measurement effect with a high signal-to-noise ratio can be achieved by using two measurement probe beams within a working distance range of 40 m–60 m. In the flight experiment, the flight trajectory calculated using the LDV-measured velocity data was compared with the global navigation satellite system (GNSS)-recorded trajectory. The result shows that LDV can achieve an odometer accuracy of 4.8‰. This study has validated the feasibility of the laser Doppler velocimeter in drone navigation and positioning, providing a novel method for reliable and high-precision velocity measurement in autonomous drone navigation. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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20 pages, 11387 KiB

Open AccessEditor’s ChoiceArticle

An Algorithm for Affordable Vision-Based GNSS-Denied Strapdown Celestial Navigation

by Samuel Teague and Javaan Chahl

Drones 2024, 8(11), 652; https://doi.org/10.3390/drones8110652 - 7 Nov 2024

Viewed by 54000

Abstract

Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of the few non-emissive modalities that enables global navigation over the ocean at night in Global Navigation Satellite System (GNSS) denied environments. This study demonstrates a modular, low cost, lightweight strapdown celestial navigation solution that is utilized in conjunction with Ardupilot running on a Cube Orange to produce position estimates to within 4 km. By performing an orbit through a full rotation of compass heading and averaging the position output, we demonstrate that the biases present in a strapdown imaging system can be nullified to drastically improve the position estimate. Furthermore, an iterative method is presented which enables the geometric alignment of the camera with the Attitude and Heading Reference System (AHRS) in-flight without an external position input. The algorithm is tested using real flight data captured from a fixed wing aircraft. The results from this study offer promise for the application of low cost celestial navigation as a redundant navigation modality in affordable, lightweight drones. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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18 pages, 5430 KiB

Open AccessArticle

Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier

by Shuzhi Liu, Houjin Lu and Seung-Hoon Hwang

Drones 2024, 8(1), 15; https://doi.org/10.3390/drones8010015 - 9 Jan 2024

Cited by 4 | Viewed by 2718

Abstract

Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the

(X, Y)

dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the

H

dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the

(X, Y, H)

dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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22 pages, 10296 KiB

Open AccessArticle

Unmanned Aerial Vehicle 3D Path Planning Based on an Improved Artificial Fish Swarm Algorithm

by Tao Zhang, Liya Yu, Shaobo Li, Fengbin Wu, Qisong Song and Xingxing Zhang

Drones 2023, 7(10), 636; https://doi.org/10.3390/drones7100636 - 16 Oct 2023

Cited by 10 | Viewed by 2949

Abstract

A well-organized path can assist unmanned aerial vehicles (UAVs) in performing tasks efficiently. The artificial fish swarm algorithm (AFSA) is a widely used intelligent optimization algorithm. However, the traditional AFSA exhibits issues of non-uniform population distribution and susceptibility to local optimization. Despite the numerous AFSA variants introduced in recent years, many of them still grapple with challenges like slow convergence rates. To tackle the UAV path planning problem more effectively, we present an improved AFSA algorithm (IAFSA), which is primarily rooted in the following considerations: (1) The prevailing AFSA variants have not entirely resolved concerns related to population distribution disparities and a predisposition for local optimization. (2) Recognizing the specific demands of the UAV path planning problem, an algorithm that can combine global search capabilities with swift convergence becomes imperative. To evaluate the performance of IAFSA, it was tested on 10 constrained benchmark functions from CEC2020; the effectiveness of the proposed strategy is verified on the UAV 3D path planning problem; and comparative algorithmic experiments of IAFSA are conducted in different maps. The results of the comparison experiments show that IAFSA has high global convergence ability and speed. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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14 pages, 2612 KiB

Open AccessArticle

Photometric Long-Range Positioning of LED Targets for Cooperative Navigation in UAVs

by Laurent Jospin, Alexis Stoven-Dubois and Davide Antonio Cucci

Drones 2019, 3(3), 69; https://doi.org/10.3390/drones3030069 - 30 Aug 2019

Cited by 6 | Viewed by 4675

Abstract

Autonomous flight with unmanned aerial vehicles (UAVs) nowadays depends on the availability and reliability of Global Navigation Satellites Systems (GNSS). In cluttered outdoor scenarios, such as narrow gorges, or near tall artificial structures, such as bridges or dams, reduced sky visibility and multipath effects compromise the quality and the trustworthiness of the GNSS position fixes, making autonomous, or even manual, flight difficult and dangerous. To overcome this problem, cooperative navigation has been proposed: a second UAV flies away from any occluding objects and in line of sight from the first and provides the latter with positioning information, removing the need for full and reliable GNSS coverage in the area of interest. In this work we use high-power light-emitting diodes (LEDs) to signalize the second drone and we present a computer vision pipeline that allows to track the second drone in real-time from a distance up to 100 m and to compute its relative position with decimeter accuracy. This is based on an extension to the classical iterative algorithm for the Perspective-n-Points problem in which the photometric error is minimized according to a image formation model. This extension allow to substantially increase the accuracy of point-feature measurements in image space (up to 0.05 pixels), which directly translates into higher positioning accuracy with respect to conventional methods. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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18 pages, 4441 KiB

Open AccessArticle

Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

by Harry A. G. Pointon, Benjamin J. McLoughlin, Christian Matthews and Frederic A. Bezombes

Drones 2019, 3(1), 19; https://doi.org/10.3390/drones3010019 - 14 Feb 2019

Cited by 19 | Viewed by 5293

Abstract

In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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12 pages, 1977 KiB

Open AccessArticle

Sensitivity to Time Delays in VDM-Based Navigation

by Gabriel Laupré, Mehran Khaghani and Jan Skaloud

Drones 2019, 3(1), 11; https://doi.org/10.3390/drones3010011 - 14 Jan 2019

Cited by 7 | Viewed by 5137

Abstract

A recently proposed navigation methodology for aerial platforms based on the vehicle dynamic model (VDM) has shown promising results in terms of navigation autonomy. Its practical realization requires that control inputs are related to the same absolute time frame as inertial measurement unit (IMU) data and all other observations when available (e.g., global navigation satellite system (GNSS) position, barometric altitude, etc.). This study analyzes the (non-) tolerances of possible delays in control-input command with respect to navigation performance on a fixed-wing unmanned aerial vehicle (UAV). Multiple simulations using two emulated trajectories based on real flights reveal the vital importance of correct time-tagging of servo data while that of motor data turned out to be tolerable to a considerably large extent. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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Review

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30 pages, 343 KiB

Open AccessEditor’s ChoiceReview

A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles

by Ines Khoufi, Anis Laouiti and Cedric Adjih

Drones 2019, 3(3), 66; https://doi.org/10.3390/drones3030066 - 24 Aug 2019

Cited by 129 | Viewed by 12041

Abstract

The use of Unmanned Aerial Vehicles (UAVs) is rapidly growing in popularity. Initially introduced for military purposes, over the past few years, UAVs and related technologies have successfully transitioned to a whole new range of civilian applications such as delivery, logistics, surveillance, entertainment, and so forth. They have opened new possibilities such as allowing operation in otherwise difficult or hazardous areas, for instance. For all applications, one foremost concern is the selection of the paths and trajectories of UAVs, and at the same time, UAVs control comes with many challenges, as they have limited energy, limited load capacity and are vulnerable to difficult weather conditions. Generally, efficiently operating a drone can be mathematically formalized as a path optimization problem under some constraints. This shares some commonalities with similar problems that have been extensively studied in the context of urban vehicles and it is only natural that the recent literature has extended the latter to fit aerial vehicle constraints. The knowledge of such problems, their formulation, the resolution methods proposed—through the variants induced specifically by UAVs features—are of interest for practitioners for any UAV application. Hence, in this study, we propose a review of existing literature devoted to such UAV path optimization problems, focusing specifically on the sub-class of problems that consider the mobility on a macroscopic scale. These are related to the two existing general classic ones—the Traveling Salesman Problem and the Vehicle Routing Problem. We analyze the recent literature that adapted the problems to the UAV context, provide an extensive classification and taxonomy of their problems and their formulation and also give a synthetic overview of the resolution techniques, performance metrics and obtained numerical results. Full article

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