Unmanned Aerial Vehicle Navigation Research Articles

An Improved SSVEP-based Brain-Computer Interface with Low Contrast Visual Stimulation and its Application in UAV Control.

Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimuli. visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of Unmanned Aerial Vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.

Flood risk assessment of the Garita River in the urban zone of San Luis Potosí City, by hydrodynamic modeling

Rapid and uncontrolled urban growth and land use changes in watersheds worldwide have led to increased surface runoff within metropolitan areas, coupled with climate change, creating a risk for residents during the rainy season. The city of San Luis Potosí is no exception to this phenomenon. One affected watercourse is the Garita Stream, which flows inside the city near urbanization. It is essential to analyze the effects of urban sprawl on this stream based on historical precipitation data for the town. Hydrological and topographical information were required to conduct this research. The hydrological study of the basin involved analyzing the region’s geomorphology and historical climatological data. For the stream’s topography, aerial photogrammetry using an unmanned aerial Vehicle (UAV) and Global Navigation Satellite System (GNSS) equipment was employed to conduct topographic surveys in the area. To find out when the Garita stream would overflow and which areas are most likely to flood, numerical modeling was done using 1D, 2D, and 3D programs like SWMM5 (Storm Water Management Model), HEC-RAS (Hydrologic Engineering Center’s River Analysis System), and EDFC Explorer (Environmental Fluid Dynamics Code). These models simulated different return periods and their correlation with current flooding events recorded in the area, thereby further proposing solutions to mitigate overflow issues. By conducting these simulations and analyzing the results, solutions can be suggested to address the overflow problems in the area based on historical flood events at various return periods caused by the Garita Stream.

Using the set of informative features of a binding object to construct a decision function by the system of technical vision when localizing mobile robots

The object of this study is the process of constructing a decision function by the optical-electronic system of technical vision under the conditions of the influence of obstacles on the current image, which is formed in the process of localization of a mobile robot. The paper reports the results of solving the problem of constructing a decision function when reducing the signal-to-noise ratio of the current image by using a set of informative features for the selection of the binding object, namely, brightness, contrast, and its area. The selection of the binding object is proposed to be carried out by choosing the appropriate values of the quantization thresholds of the current image for the selected informative features, taking into account the signal-to-noise ratio, which provides the necessary probability of object selection. The dependence of the object selection probability on the selected values of the quantization thresholds was established. The use of the results could enable the construction of a unimodal decision function when localizing mobile robots on imaging surfaces with weakly pronounced brightness and contrast characteristics of objects, as well as with their small geometric dimensions. By modeling, the probability of forming a decision function was estimated depending on the degree of noise of the current images. It is shown that the application of the proposed approach allows selection of objects with a probability ranging from 0.78 to 0.99 for the values of the signal-to-noise ratio of images formed by the technical vision system under real conditions. The method to construct a decision function under the influence of interference could be implemented in information processing algorithms used in optical-electronic technical vision systems for the navigation of unmanned aerial vehicles

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify objects. For the indoor positioning techniques, we employed visual-inertial odometry (VIO), ultra-wideband (UWB), AprilTag fiducial markers, and simultaneous localization and mapping (SLAM). These algorithms included global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. For UAV navigation, we combined the SLAM-based RTAB-map indoor mapping and navigation path planning of the ROS for indoor environments. This system enabled precise drone positioning indoors and utilized global and local path planners to generate flight paths that avoided dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve warehouse inventory inspection, a reinforcement learning approach was proposed, recognizing markers by adjusting the UAV’s viewpoint. We addressed several of the main problems in inventory management, including efficiently planning of paths, while ensuring a certain detection rate. Two reinforcement learning techniques, AC (actor–critic) and PPO (proximal policy optimization), were implemented based on AprilTag identification. Testing was performed in both simulated and real-world environments, and the effectiveness of the proposed method was validated.

A low-complexity ISAC method for LFM-PSK signals using de-chirping and ISAC phase unwrapping techniques

Integrated Sensing and Communication (ISAC) has the potential to revolutionize wireless technology in areas such as autonomous vehicles, the Internet of Things, and unmanned aerial vehicle navigation technology. Nonetheless, conventional processing methods for Linear Frequency Modulation-Phase Shift Keying (LFM-PSK) signals predominantly depend on matched filtering, leading to high sampling rates and computational complexity. In response to these challenges, our research proposes a method to process LFM-PSK signals, which enables the use of de-chirping techniques. Applying the proposed ISAC unwrapping technique makes it possible to selectively recover the phase of the de-chirped LFM-PSK signal. Subsequently, the distance can be calculated by analyzing the slope of the recovered phase. Moreover, by adjusting the threshold in the ISAC unwrapping technique, communication information can be extracted by detecting phase hops associated with PSK. This method offers an attractive alternative to traditional methods such as matched filtering and coherent demodulation techniques. We derive the Cramér-Rao lower bound (CRLB) for ranging. Theoretical analysis and simulation indicate that our method effectively reduces the required sampling rate and provides higher-ranging performance than the matched filtering technique in radar sensing. In high signal-to-noise ratio scenarios, our proposed method delivers a ranging performance close to that of CRLB. Regarding communication, our method has the advantage of lower synchronization requirements, making it more feasible for practical engineering applications than the conventional coherent demodulation method. Furthermore, it also necessitates lower computational resources compared to matched filtering techniques. Importantly, our method allows high device sharing, promoting cost-effective ISAC implementation.

Deep-learning based autonomous-exploration for UAV navigation

Unmanned Aerial Vehicles (UAVs), functioning as aerial robots, undertake multifarious missions within intricate and uncharted environments, necessitating autonomous exploration for efficient UAV navigation. This paper introduces a Deep Learning-based Autonomous UAV Exploration method (DLAE), which distinguishes itself from traditional autonomous ground robot exploration by employing cameras instead of radar sensors. This difference propels us toward proposing a hybrid action space that integrates both positional and yaw actions, aimed at circumventing the limitations imposed by the Field of View (FOV). Furthermore, to enhance learning efficiency in autonomous UAV exploration, we design an invalid action masking scheme. Significantly, our contribution includes the development of an autoregressive network model alongside a distinct training procedure specifically tailored for autonomous UAV exploration. Comprehensive experiments conducted across a variety of test maps reveal that our proposed method surpasses existing learning-based approaches, delivering superior exploration efficiency and reduced decision-making time in comparison to conventional strategies. Moreover, physical simulations have corroborated the effectiveness of our method in practical scenarios, showcasing its potential for real-world applications.

Unmanned Aerial Vehicle Path Planning with Hybrid Motion Algorithm for Obstacle Avoidance

Unmanned aerial vehicles (UAVs) are gaining prominence in autonomously navigating diverse terrains, requiring the capability to establish collision-free trajectories and adapt them on-the-fly to changing environments. This study's central contribution lies in devising an optimized motion planning framework tailored for UAVs operating amidst dynamic scenarios. This framework comprises two integral components: an optimized motion planner and a dynamic scenario generator. To enhance trajectory optimization, the optimized motion planner enhances the Rapidly-exploring Random Tree (RRTX) method with a Covariant Hamiltonian Optimization for Motion Planning (CHOMP) algorithm-based optimizer. Addressing the challenges posed by dynamic environments characterized by abrupt appearance, disappearance, or shifting of constraints, the motion planner adeptly identifies environmental changes and computes collision-free paths during UAV navigation. The dynamic scenario generator integrates a UAV simulator and barrier information, effectively emulating UAV obstacles and intended flight patterns within a Unity-based simulation environment. The simulator employed is Flight Mare, a versatile quadrotor simulator that employs Unity's graphics engine and a physics engine for dynamic simulations. Through comprehensive simulations, the proposed approach is validated, demonstrating its efficacy in enabling UAVs to autonomously navigate dynamic environments while avoiding obstacles successfully.

Landslide Deposit Erosion and Reworking Documented by Geomatic Surveys at Mount Meager, BC, Canada

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems.

VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments

Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders provide a limited environmental overview, and LiDAR is energy-intensive. To address these challenges, this paper proposes VizNav, a modular DRL-based framework for autonomous UAV navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay and Importance Sampling (PER) to improve performance in continuous action spaces and mitigate overestimations. Additionally, VizNav employs depth map images (DMIs) to enhance visual navigation by accurately estimating objects’ depth information, thereby improving obstacle avoidance. Empirical results show that VizNav, by leveraging TD3, improves navigation, and the inclusion of PER and DMI further boosts performance. Furthermore, the deployment of VizNav across various experimental settings confirms its flexibility and adaptability. The framework’s architecture separates the agent’s learning from the training process, facilitating integration with various DRL algorithms, simulation environments, and reward functions. This modularity creates a potential to influence RL simulation in various autonomous navigation systems, including robotics control and autonomous vehicles.

An Improved Aquila Optimizer with Local Escaping Operator and Its Application in UAV Path Planning

Background: With the development of intelligent technology, Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields. Path planning is the most important part of UAV navigation system. Its purpose is to find a smooth and feasible path from the start to the end. Objective: In order to obtain a better flight path, this paper presents an improved Aquila optimizer combing the opposition-based learning and the local escaping operator, named LEOAO, to deal with the UAV path planning problem in three-dimensional environments. Methods: UAV path planning is modelled as a constrained optimization problem in which the cost function consists of one objective: path length and four constraints: safe distance, flight height, turning angle and climbing/diving angle. In this paper, the LEOAO is introduced to find the optimal path by minimizing the cost function, and B-Spline is invited to represent a smooth path. The local escaping operator is used to enhance the search ability of the algorithm. Results: To test the performance of LEOAO, two scenarios are applied based on basic terrain function. Experiments show that the proposed LEOAO outperforms other algorithms such as the grey wolf optimizer, whale optimization algorithm, including the original Aquila optimizer. Conclusion: The proposed algorithm combines the opposition-based learning and local escaping operator. The opposition-based learning algorithm has the ability to accelerate convergence. And the introduction of LEO effectively balances the exploration and exploitation abilities of the algorithm and improves the quality of the population. Finally, the improved Aquila optimizer obtains a better path.

An Anonymous Authenticated Group Key Agreement Scheme for Transfer Learning Edge Services Systems

The visual information processing technology based on deep learning (DL) can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning (TL) to unload deep neural networks (DNN) to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity is still challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of UAV authentication protocol and collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

A semantic SLAM-based method for navigation and landing of UAVs in indoor environments

Autonomous navigation and landing of Unmanned aerial vehicles (UAVs) are critical functions towards full intelligence in unknown environments. However, current methods heavily rely on positioning devices or geometric maps, leading to two limitations: (1) UAVs fail to work normally indoors when signals are blocked, and (2) Navigation systems are not capable of achieving high-level decision-making missions. In this paper, we propose a novel and complete framework to realize the autonomous landing of UAVs in unknown indoor scenes based on visual SLAM, semantic segmentation, terrain estimation, and a decision-making model. Firstly, our 3D map is built on top of the visual SLAM system with semantic features, and then multiple types of maps (e.g., texture map, octo-map, grid-map, and semantic topology) are created for different function requirements. To achieve high-level scene understanding, we design a data association rule to fuse semantic features extracted by a deep learning model into the mapping process of topology structure. Next, a terrain estimation strategy is performed in the lightweight grid-map, which is modeled only via low-level elevation representation. Through multiple terrain constraints factor, a few optimized landing sites are selected in safe regions via clustering algorithm. Finally, based on the guidance of semantic topology, we construct a decision-making model for UAV landing that effectively integrates terrain safety, environment perception, and path planning. We perform extensive autonomous landing experiments on multiple indoor scenes from the TUM RGB-D dataset, and the full unknown environment maps are created simultaneously. The experiment proves that the proposed framework can select an optimal landing site for the advanced mission of UAVs effectively.

Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios

Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task. Recently emerged, vision-based navigation has been a promising and feasible alternative to GNSS-based navigation. However, existing vision-based techniques are inadequate in addressing flight deviation caused by environmental disturbances and inaccurate position predictions in practical settings. In this paper, we present a novel angle robustness navigation paradigm to deal with flight deviation in point-to-point navigation tasks. Additionally, we propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module to accurately predict direction angles for high-precision navigation. To evaluate the vision-based navigation methods, we collect a new dataset termed as UAV_AR368. Furthermore, we design the Simulation Flight Testing Instrument (SFTI) using Google Earth to simulate different flight environments, thereby reducing the expenses associated with real flight testing. Experiment results demonstrate that the proposed model outperforms the state-of-the-art by achieving improvements of 26.0% and 45.6% in the success rate of arrival under ideal and disturbed circumstances, respectively.

A Review of Electric UAV Visual Detection and Navigation Technologies for Emergency Rescue Missions

Sudden disasters often result in significant losses of human lives and property, and emergency rescue is a necessary response to disasters. In recent years, with the development of electric unmanned aerial vehicles (UAVs) and artificial intelligence technology, the combination of these technologies has been gradually applied to emergency rescue missions. However, in the face of the complex working conditions of emergency rescue missions, the application of electric UAV visual detection still faces great challenges, particularly in relation to a lack of GPS positioning signal in closed emergency rescue environments, as well as unforeseen obstacle avoidance and autonomous planning and searching flights. Although the combination of visual detection and visual navigation technology shows great potential and added value for use in the context of emergency rescue, at present it remains in the research and experimental stages. Consequently, this paper summarizes and discusses the current status and development of visual detection and navigation technologies for electric UAVs, as well as issues related to emergency rescue applications, with a view to accelerating the research and application of visual detection and navigation technologies for electric UAVs in emergency rescue missions. In this study, we first summarize the classification of typical disasters, analyze the application of sample UAV and configurations in typical disasters with a high frequency of occurrence, refine key electric UAV technologies in emergency rescue missions, and propose the value of exploring electric UAV visual detection and navigation technologies. Subsequently, current research on electric UAV visual detection and navigation technology is analyzed and its application in emergency rescue missions is discussed. Finally, this paper presents the problems faced in the application of electric UAV visual detection and navigation technology in urban emergency rescue environments and offers insights into future research directions.

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.

Study of on-board equipment of modern uavs

This paper analyzes the development and production of modern unmanned aerial vehicles (UAVs). It is noted that they have the following main systems: glider; propulsion system; power supply system; management system; navigation system; telemetry system; radio communication system. All UAV systems are interconnected and work as one complex mechanism. Depending on the list of combat tasks to be solved, the following devices can be additionally installed on board the UAV: optical-electronic, thermal imaging, radar, radiotechnical, radiation, chemical, bacteriological and other types of reconnaissance systems with small intelligence storage systems; means of setting active radio-electronic jammers; means of aiming and adjusting guided weapons; various means of defeat; means of control and communication with the ground control point; responsible for the state identification system; autonomous flight and automatic landing devices. The work reveals the features of the UAV engine design, it is noted that 4, 6 or more engines are installed on modern helicopter-type UAVs, so-called "multicopters", "quadracopters", "drones". In this work, it is noted that the UAV navigation equipment can have various level of complexity and use several signals coming from sensors of different physical origins to calculate its location. The features of the UAV radio communication system are revealed, which is a set of various lines through which intelligence information of various levels of importance and protection is transmitted. All communication links can use different frequency bands and different relay modes, use different signal-code designs, specially adapted to the importance of the intelligence information being transmitted. It was noted that in the absence of a control command, the UAV goes into autonomous flight mode.

Research on Unmanned Aerial Vehicle Path Planning

As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.

Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture

Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.