Vision-based autonomous landing for rotorcraft unmanned aerial vehicle

Bu, Chaoyan; Ai, Yunfeng; Du, Huajun

doi:10.1109/icves.2016.7548174

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Although the traditional radar-based guidance system and optical landing system are widely utilized [1][2][3], these systems have many limitations that fail to satisfy allweather requirements. In addition, there are some visualbased guidance technologies for automatic carrier landing [4][5][6][7][8], but the visual-based method is too difficult to implement in the visibility constrained environment.…”

Section: Introductionmentioning

confidence: 99%

Beidou Receiver Based on Anti-Jamming Antenna Arrays with Self-Calibration for Precise Relative Positioning

An,

Kang,

Ban

et al. 2024

J. of Syst. Eng. Electron.

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Unmanned aerial vehicles (UAVs) may be subjected to unintentional radio frequency interference (RFI) or hostile jamming attack which will lead to fail to track global navigation satellite system (GNSS) signals. Therefore, the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem. In this paper, a distortionless phase digital beamforming (DBF) algorithm with self-calibration antenna arrays is proposed, which enables to obtain distortionless carrier phase while suppressing jamming. Additionally, architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed. Finally, the performance of the algorithm is evaluated, including antenna calibration accuracy, carrier phase distortionless accuracy, and carrier phase measurement accuracy without jamming. Meanwhile, the maximal jamming to signal ratio (JSR) and real time kinematic (RTK) positioning accuracy under wideband jamming are also investigated. The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.

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Section: Introductionmentioning

confidence: 99%

Beidou Receiver Based on Anti-Jamming Antenna Arrays with Self-Calibration for Precise Relative Positioning

An,

Kang,

Ban

et al. 2024

J. of Syst. Eng. Electron.

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show abstract

“…Visual navigation, as an effective navigation technology, receives increasing attentions. It mainly uses computer vision technology to obtain positioning parameter information [19,20]. This technology can obtain information through common low-cost devices and has broad application prospects in the field of UAV navigation.…”

Section: Introductionmentioning

confidence: 99%

Precision Landing Test and Simulation of the Agricultural UAV on Apron

Guo

Liu

et al. 2020

Sensors

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Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields.

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“…Among the many articles discussed above, only the following few consider the take-off, landing and navigation as a part of their overall architecture: [88], [85] and [102]. For more details the reader is invited to refer to the following surveys and featured articles: [108], [109], [110].…”

Section: Literature Review:vision-based Localisationmentioning

confidence: 99%

Multi-robot relative localisation using computer vision

Ulun¹

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This thesis focusses on robots with a marsupial relationship that use computer vision as their main sensor. This work is focused on two separate projects.In computer vision, drift is still a major drawback when using vision-based localisation methods. The first project of this thesis combines the position information from two Unmanned Ground Vehicles (UGV) to reduce this drift in GPS denied environments. Relative pose measurements between two robots are used in a similar way to loop-closure. The robots start at a nearby location and share a common field of view. The sharing of the information is done periodically. The pose of each robot is calculated using visual odometry. The relative position of the robots is calculated using both monocular and stereo image pairs, for initial estimation and refining respectively. A specific algorithm is proposed to combine the visual odometry and relative pose estimations from one robot to another in a pose graph optimisation scheme to reduce the drift. Experiments have been conducted on both simulations and real-life.The second project introduces two new methods that enabled the autonomous take-off, tracking and precise landing with an Unmanned Aerial Vehicle (UAV) on a UGV using a dual monocular camera setup. These methods used predefined markers to compute the relative position of the UAV from the landing platform that is attached to the UGV. The cameras on the UAV have different focal lengths, which are providing better performance than using a single camera. Different N T U S xviii LIST OF FIGURES 3.8 Trajectories of RobotA and RobotB for the first simulation scenario . 44 3.9 Trajectory output for the first simulation scenario . . . . . . . . . . 45 3.10 Trajectories of RobotA and RobotB for the second simulation scenario 46 3.11 Trajectory output for the second simulation scenario .

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Vision-based autonomous landing for rotorcraft unmanned aerial vehicle

Cited by 3 publications

References 23 publications

Beidou Receiver Based on Anti-Jamming Antenna Arrays with Self-Calibration for Precise Relative Positioning

Beidou Receiver Based on Anti-Jamming Antenna Arrays with Self-Calibration for Precise Relative Positioning

Precision Landing Test and Simulation of the Agricultural UAV on Apron

Multi-robot relative localisation using computer vision

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