Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection

Ren, Jin; Owais, Hafiz Muhammad; Lin, Defu; Song, Tao; Yuán, Yǒulù

doi:10.1002/rob.21814

Cited by 32 publications

(16 citation statements)

References 32 publications

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“…The detection results during the landing are shown in Figure 3. The target detection algorithm is not the focus of this paper, and you can refer to [38] for extra details. One drawback of the vision sensor described above is its limited detection range.…”

Section: Detection and State Estimation Of The Landing Platformmentioning

confidence: 99%

“…The filter reduces noise in the measured values. Symbol G 1 represents the control matrix of the outer loop, which can be derived by the Taylor expansion of Equation ( 5); see [38]. Finally, v t is the virtual volume of the position deviation under the action of a proportional-differential (PD) controller, and it can be expressed as follows,…”

Section: Cascade Indi Controller Designmentioning

confidence: 99%

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Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

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Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

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Section: Detection and State Estimation Of The Landing Platformmentioning

confidence: 99%

Section: Cascade Indi Controller Designmentioning

confidence: 99%

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

Self Cite

View full text Add to dashboard Cite

show abstract

“…The vision algorithm is comprised of adaptive thresholding, fast undistortion, ellipsecross pattern detection, and relative pose estimation, details of which were comprehensively analyzed in [ 22 ]. Besides, the vision systems of other participants are revealed in [ 10 , 23 , 24 ]. Though the MBZIRC competition offered a valuable opportunity to examine the UAVs’ performance when facing challenging real-world conditions, nighttime landing was still a task beyond its consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They are the demonstration or proof-of-concept that could light a path toward more practical and realistic solutions [ 7 , 8 ]. Some of the recent studies have carried out flight tests in real-world environments where well-illuminated scenes favor the vision systems [ 9 , 10 ]. With the proliferation of UAV applications, there emerges an increasing need to operate UAVs at nighttime, benefiting from less airspace traffic and less human-activity-based interferences.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

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Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.

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“…GPS is also unreliable in closed spaces and other GPS-denied environments. The use of onboard cameras and computer vision techniques for object tracking and localization is a popular way to overcome such shortcomings to enable autonomous landing not only on stationary but mobile targets as well [ 7 , 8 , 9 ]. Solutions for detecting the target using other sensors like light detection and ranging (LIDAR) [ 10 ], ultrasonic sensors, and IR sensors [ 11 ] are also available in the literature.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Quadcopter Landing on a Moving Target

Gautam

Singh

Sujit

et al. 2022

Sensors

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Autonomous landing on a moving target is challenging because of external disturbances and localization errors. In this paper, we present a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared to that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad. No prior information about the landing target is needed. The guidance is based on pure pursuit guidance law. The convergence of the closing velocity controller is shown, and we test the efficacy of the proposed approach through simulations and field experiments. The landing target during the field experiments was manually dragged with a maximum speed of 0.6 m/s. In the simulations, the maximum target speed of the ground vehicle was 3 m/s. We conducted a total of 27 field experiment runs for landing on a moving target and achieved a successful landing in 22 cases. The maximum error magnitude for successful landing was recorded to be 35 cm from the landing target center. For the failure cases, the maximum distance of vehicle landing position from target boundary was 60 cm.

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Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection

Cited by 32 publications

References 32 publications

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Autonomous Quadcopter Landing on a Moving Target

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