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Drones
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Autonomous Drone Navigation in GPS-Denied Environments
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Autonomous Drone Navigation in GPS-Denied Environments

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: 26 December 2025 | Viewed by 16137

Special Issue Editors

Prof. Dr. Hector Gutierrez

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA
Interests: aerospace systems; mechatronics; motion control; electromechanical devices
Prof. Dr. Yang Liu

E-Mail Website
Guest Editor
Department of Engineering, University of Exeter, Exeter EX4 4PY, UK
Interests: automation and robotics; advanced control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drones have important applications in indoor environments, where there is no access to GPS for precise positioning. Indoor drone navigation faces specific challenges to achieve precise, robust position and attitude control, particularly given the small distances to walls and obstacles. While there is extensive work in the literature regarding localization and mapping, indoor navigation remains an important open topic with distinct challenges, given the reliance on visual–inertial information to achieve precise, robust positioning and 6-DOF motion control.

The Special Issue focuses on software, hardware, and analytical and computational techniques that support drone navigation in indoor environments, including, but not limited to, the following: image processing, sensor fusion, online–offline sensor calibration, localization, mapping, position and attitude estimation, path planning, and the 6-DOF motion control of drones in indoor environments.

Suggested article types:

  • Review Articles: detailed review of the state of the art in indoor drone navigation.
  • Articles: novel research contributions

Prof. Dr. Hector Gutierrez
Prof. Dr. Yang Liu
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • autonomous drone navigation
  • GPS-denied environment
  • visual–inertial navigation
  • visual–inertial odometry
  • simultaneous localization and mapping
  • resource-constrained autonomous navigation
  • robust dynamic sensor calibration

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Published Papers (7 papers)

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24 pages, 21171 KB  
Article
Long-Duration Inspection of GNSS-Denied Environments with a Tethered UAV-UGV Marsupial System
by Simón Martínez-Rozas, David Alejo, José Javier Carpio, Fernando Caballero and Luis Merino
Drones 2025, 9(11), 765; https://doi.org/10.3390/drones9110765 - 5 Nov 2025
Viewed by 813
Abstract
Unmanned Aerial Vehicles (UAVs) have become essential tools in inspection and emergency response operations due to their high maneuverability and ability to access hard-to-reach areas. However, their limited battery life significantly restricts their use in long-duration missions. This paper presents a tethered marsupial [...] Read more.
Unmanned Aerial Vehicles (UAVs) have become essential tools in inspection and emergency response operations due to their high maneuverability and ability to access hard-to-reach areas. However, their limited battery life significantly restricts their use in long-duration missions. This paper presents a tethered marsupial robotic system composed of a UAV and an Unmanned Ground Vehicle (UGV), specifically designed for autonomous, long-duration inspection tasks in Global Navigation Satellite System (GNSS)-denied environments. The system extends the UAV’s operational time by supplying power through a tether connected to high-capacity battery packs carried by the UGV. Our work details the hardware architecture based on off-the-shelf components to ensure replicability and describes our full-stack software framework used by the system, which is composed of open-source components and built upon the Robot Operating System (ROS). The proposed software architecture enables precise localization using a Direct LiDAR Localization (DLL) method and ensures safe path planning and coordinated trajectory tracking for the integrated UGV–tether–UAV system. We validate the system through three sets of field experiments involving (i) three manual flight endurance tests to estimate the operational duration, (ii) three experiments for validating the localization and the trajectory tracking systems, and (iii) three executions of an inspection mission to demonstrate autonomous inspection capabilities. The results of the experiments confirm the robustness and autonomy of the system in GNSS-denied environments. Finally, all experimental data have been made publicly available to support reproducibility and to serve as a common open dataset for benchmarking. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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15 pages, 2966 KB  
Article
Depth-Based Safe Landing for Unmanned Aerial Vehicles in GPS-Denied Environment
by Miguel Angel Cerda, Jonathan Flores, Sergio Salazar, Iván González-Hernández and Rogelio Lozano
Drones 2025, 9(11), 764; https://doi.org/10.3390/drones9110764 - 5 Nov 2025
Viewed by 669
Abstract
This paper presents a safe landing methodology for Unmanned Aerial Vehicles (UAVs) when the GPS-based navigation system fails or is denied or unavailable. The approach relies on the estimation of a flat landing area when landing is required in an unknown area. The [...] Read more.
This paper presents a safe landing methodology for Unmanned Aerial Vehicles (UAVs) when the GPS-based navigation system fails or is denied or unavailable. The approach relies on the estimation of a flat landing area when landing is required in an unknown area. The proposed system is based on a lightweight computer vision algorithm that enables real-time identification of suitable landing zones using a depth camera and an onboard companion computer. Analysis of small, spatially distributed areas to calculate the mean altitude and standard deviation across regions enables reliable selection of flat surfaces. A robust landing control algorithm is activated when the area meets strict flatness conditions for a continuous period. Real-time experiments confirmed the effectiveness of this approach under disturbances, showing reliable detection of the safe zone and the robustness of the proposed control algorithm in outdoor environments. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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23 pages, 11567 KB  
Article
Georeferenced UAV Localization in Mountainous Terrain Under GNSS-Denied Conditions
by Inseop Lee, Chang-Ky Sung, Hyungsub Lee, Seongho Nam, Juhyun Oh, Keunuk Lee and Chansik Park
Drones 2025, 9(10), 709; https://doi.org/10.3390/drones9100709 - 14 Oct 2025
Cited by 1 | Viewed by 1392
Abstract
In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with [...] Read more.
In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with Scene Matching (SM) to achieve robust positioning. The method employs a camera projection model combined with Digital Elevation Model (DEM) to orthorectify UAV images, thereby mitigating distortions from central projection and terrain relief. Pre-processing steps enhance consistency with reference orthophoto maps, after which template matching is performed using normalized cross-correlation (NCC). Sensor fusion is achieved through extended Kalman filters (EKFs) incorporating Inertial Navigation System (INS), GNSS (when available), barometric altimeter, and SM outputs. The framework was validated through flight tests with an aircraft over 45 km trajectories at altitudes of 2.5 km and 3.5 km in mountainous terrain. The results demonstrate that orthorectification improves image similarity and significantly reduces localization error, yielding lower 2D RMSE compared to conventional rectification. The proposed approach enhances VBN by mitigating terrain-induced distortions, providing a practical solution for UAV localization in GNSS-denied scenarios. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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26 pages, 8635 KB  
Article
Test Methodologies for Collision Tolerance, Navigation, and Trajectory-Following Capabilities of Small Unmanned Aerial Systems
by Edwin Meriaux and Kshitij Jerath
Drones 2025, 9(6), 447; https://doi.org/10.3390/drones9060447 - 18 Jun 2025
Viewed by 1088
Abstract
SmallUnmanned Aerial Systems (sUAS) have seen rapid adoption thanks to advances in endurance, communications, autonomy, and manufacturing costs, yet most testing remains focused on GPS-supported, above-ground operations. This study introduces new test methodologies and presents comprehensive experimental evaluations of collision tolerance, navigation, and [...] Read more.
SmallUnmanned Aerial Systems (sUAS) have seen rapid adoption thanks to advances in endurance, communications, autonomy, and manufacturing costs, yet most testing remains focused on GPS-supported, above-ground operations. This study introduces new test methodologies and presents comprehensive experimental evaluations of collision tolerance, navigation, and trajectory following for commercial sUAS platforms in GPS-denied indoor environments. We also propose numerical and categorical metrics—based on established vehicle collision protocols such as the Modified Acceleration Severity Index (MASI) and Maximum Delta V (MDV)—to quantify collision resilience; for example, the tested platforms achieved an average MASI of 0.1 g, while demonstrating clear separation between the highest- and lowest-performing systems. The experimental results revealed that performance varied significantly with mission complexity, obstacle proximity, and trajectory requirements, identifying platforms best suited for subterranean or crowded indoor applications. By aggregating these metrics, users can select the optimal drone for their specific mission requirements in challenging enclosed spaces. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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20 pages, 7513 KB  
Article
UAV Autonomous Navigation System Based on Air–Ground Collaboration in GPS-Denied Environments
by Pengyu Yue, Jing Xin, Yan Huang, Jiahang Zhao, Christopher Zhang, Wei Chen and Mao Shan
Drones 2025, 9(6), 442; https://doi.org/10.3390/drones9060442 - 16 Jun 2025
Cited by 6 | Viewed by 5489
Abstract
This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, [...] Read more.
This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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16 pages, 4008 KB  
Article
On the Flying Accuracy of Miniature Drones in Indoor Environments
by Nusin Akram, Ilker Kocabas and Orhan Dagdeviren
Drones 2025, 9(6), 399; https://doi.org/10.3390/drones9060399 - 28 May 2025
Viewed by 2181
Abstract
Micro drones are becoming more popular in many areas, because they are small and fast enough to fly in tight and complex spaces. But they still have some significant problems. Their batteries drain fast, they cannot carry much weight, and their sensors and [...] Read more.
Micro drones are becoming more popular in many areas, because they are small and fast enough to fly in tight and complex spaces. But they still have some significant problems. Their batteries drain fast, they cannot carry much weight, and their sensors and computers are limited. These problems affect their flying performance and stability, which is very important for their missions. In this study, we evaluated the accuracy of mini drones in indoor environments. During hovering, the drones showed an average deviation of 77.9 cm, with a standard deviation of 26.4 cm, indicating moderate stability while stationary. In simple forward flights over 3 m, the average deviation increased to 92.6 cm, which showed slight drop in accuracy during movement. For more complex flight paths, such as L-shaped and square trajectories, the deviations increased to 141 cm and 245 cm, respectively. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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24 pages, 9850 KB  
Article
RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization
by Pengfei Tong, Xuerong Yang, Xuanzhi Peng and Longfei Wang
Drones 2025, 9(1), 37; https://doi.org/10.3390/drones9010037 - 7 Jan 2025
Viewed by 1584
Abstract
In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial vehicle [...] Read more.
In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial vehicle (UAV) positioning method that is capable of substituting or temporarily replacing GNSS positioning. This study proposes a reliable UAV top-down absolute positioning method (RTAPM) based on a monocular RGB camera that employs joint optimization and visual–inertial assistance. The proposed method employs a bird’s-eye view monocular RGB camera to estimate the UAV’s moving position. By comparing real-time aerial images with pre-existing satellite images of the flight area, utilizing components such as template geo-registration, UAV motion constraints, point–line image matching, and joint state estimation, a method is provided to substitute satellites and obtain short-term absolute positioning information of UAVs in challenging and dynamic environments. Based on two open-source datasets and real-time flight experimental tests, the method proposed in this study has significant advantages in positioning accuracy and system robustness over existing typical UAV absolute positioning methods, and it can temporarily replace GNSS for application in challenging environments such as disaster aid or forest rescue. Full article
(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)
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