A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms

Sandamini, Chamali; Maduranga, Madduma Wellalage Pasan; Tilwari, Valmik; Yahaya, Jamaiah; Qamar, Faizan; Nguyễn, Quang Ngọc; Ibrahim, Siti Rohana Ahmad

doi:10.3390/electronics12071533

Cited by 19 publications

(7 citation statements)

References 100 publications

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“…High-accuracy localization for drones in indoor environments has become a critical task [ 13 , 14 , 15 , 16 ]. There are various use cases where it is crucial for drones to precisely determine their location indoors in order to perform autonomous navigation and execute tasks effectively [ 17 , 18 , 19 , 20 ].…”

Section: Motivationmentioning

confidence: 99%

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.

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Section: Motivationmentioning

confidence: 99%

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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“…To overcome these challenges, researchers are exploring various new methods and technologies. For example, some studies use deep learning techniques to improve the detection and matching performance of feature points [22][23][24]. Some studies also consider using multimodal sensor fusion to improve positioning accuracy and robustness [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

Geng,

Yang,

Liu

2024

JAI

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This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV's actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV's physical coordinates. The method achieves real-time positioning and tracking during UAV flight, with experimental results demonstrating that within an acceptable error range, the UAV coordinates calculated using the proposed algorithm are consistent with the actual coordinates. The maximum error along the x-axis, y-axis, and z-axis is 4.501 cm, with the horizontal error exhibiting high stationarity and the vertical error having a low average value of 0.041 cm. The real-time positioning algorithm presented in this article possesses characteristics such as simplicity, ease of implementation, and low error, making it suitable for UAVs with limited computational processing power.

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“…The localization of a UAV is the process of determining its position in a given environment, involving the obtainment of accurate information about the UAV's spatial coordinates (i.e., latitude, longitude, orientation, etc.). In relation to other navigation hierarchies (e.g., path planning), it enables a precise execution of the planned paths and strategies [141][142][143][144][145]. Concerning vision-based UAV localization techniques, they can be characterized according to two main sets as follows: Relative Visual Localization (RVL) [146,147] and Absolute Visual Localization (AVL) [148].…”

Section: Localizationmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms

Cited by 19 publications

References 100 publications

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

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