A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures

Qiu, Ziyi; Lv, Junning; Lin, Defu; Yu, Yinan; Sun, Zhiwen; Zheng, Zhangxiong

doi:10.3390/app13010414

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Bautista et al [53] developed a system combining a vision-based photogrammetric position sensor and visual inertial odometry for precise quadcopter landing, while Wang et al [54] demonstrated a 74.61% improvement in positioning accuracy using a LiDAR-aided integrated navigation system. Additionally, Qiu et al [55] introduced a LiDAR-inertial navigation system that effectively addresses the challenges in feature extraction within spatial grid structures, enhancing pose estimation accuracy in GNSS-denied settings. These studies collectively highlight the progress and potential of sensor integration in unmanned aerial vehicles for reliable and precise indoor navigation.…”

Section: Techniques For Drone Positioning and Attitudementioning

confidence: 99%

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

Kucukayan,

Karacan

2024

Sensors

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In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as “You Only Look Once” (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model’s accuracy and the reliability of indoor human detection in real-time drone applications.

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Section: Techniques For Drone Positioning and Attitudementioning

confidence: 99%

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

Kucukayan,

Karacan

2024

Sensors

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“…In addition, the decrease in accuracy may also be caused by the algorithm's inability to accurately extract feature points. Qiu Ziyi et al [30] proposed a 3D reconstruction method based on grid and shell feature algorithms, which feature extraction is used to complete pose (position and direction) calculations based on local collinearity and coplanarity, achieving fast and accurate 3D reconstruction of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Research on 3D reconstruction technology of oblique images of closed coal yards

Cheng,

Yin,

Wang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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In China, coal-fired power plants, which mainly rely on coal, require timely inventory of coal storage to calculate economic benefits. How to achieve accurate measurement of coal storage has always been a difficult problem that coal-fired power plants urgently need to solve. For precise measurement of coal storage capacity of unmanned aerial vehicles (UAVs) in closed coal yards, the three-dimensional model through the three-dimensional (3D) reconstruction of oblique images is a highly valuable research path for engineering applications. This paper studies on the 3D reconstruction technology of oblique images based on UAVs vision. In terms of image enhancement, an enhancement function with offset is introduced to highlight image edges while preserving background information. For image feature extraction and matching, speeded up robust feature (SURF) image recognition and square filtering integral graph acceleration are adopted to meet real-time and robustness requirements. When extending dense point clouds with rich details in sparse point clouds, the area adaptive algorithm is used to meet the requirements of regional smoothing and surface changes. In the Poisson algorithm for 3D mesh reconstruction, Gaussian filtering is used instead of 3rd-order mean filtering to maintain the surface topology of the point cloud model. A 3D reconstruction of a closed coal yard in a coal-fired power plant was carried out using UAVs, and the results showed that the accuracy of the algorithm was improved by 4.5%, and the integrity by 2.22%. This technical solution meets the requirements of UAVs oblique image 3D reconstruction in closed coal yards, achieving accurate coal quantity statistics, timely inventory and transportation, stable boiler combustion, and successfully improving the accuracy and completeness of visual based 3D reconstruction.

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“…Unlike other robots (i.e., copters [4,5]), fixed-wing aircraft cannot come to a full stop midair because they have to maintain a high speed to generate lift. With their huge amount of inertia, airborne aircraft cannot brake at will unless the spoilers produce a limited amount of drag.…”

Section: Introductionmentioning

confidence: 99%

Online Motion Planning for Fixed-Wing Aircraft in Precise Automatic Landing on Mobile Platforms

Liang,

Wang,

Wang

2023

Drones

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This paper proposes the creative idea that an unmanned fixed-wing aircraft should automatically adjust its 3D landing trajectory online to land on a given touchdown point, instead of following a pre-designed fixed glide slope angle or a landing path composed of two waypoints. A fixed-wing aircraft is a typical under-actuated and nonholonomic constrained system, and its landing procedure—which involves complex kinematic and dynamic constraints—is challenging, especially in some scenarios such as landing on an aircraft carrier, which has a runway that is very short and narrow. The conventional solution of setting a very conservative landing path in advance and controlling the aircraft to follow it without dynamic adjustment of the reference path has not performed satisfactorily due to the variation in initial states and widespread environmental uncertainties. The motion planner shown in this study can adjust an aircraft’s landing trajectory online and guide the aircraft to land at a given fixed or moving point while conforming to the strict constraints. Such a planner is composed of two parts: one is used to generate a series of motion primitives which conform to the dynamic constraints, and the other is used to evaluate those primitives and choose the best one for the aircraft to execute. In this paper, numerical simulations demonstrate that when given a landing configuration composed of position, altitude, and direction, the planner can provide a feasible guidance path for the aircraft to land accurately.

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A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures

Cited by 3 publications

References 21 publications

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

Research on 3D reconstruction technology of oblique images of closed coal yards

Online Motion Planning for Fixed-Wing Aircraft in Precise Automatic Landing on Mobile Platforms

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