Close-range vision navigation and guidance for rotary UAV autonomous landing

Jung, Youeyun; Bang, Hyochoong

doi:10.1109/coase.2015.7294102

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…Then the errors in terms of the cost function equation 9are measured. The error gradients for the weights and biases are computed by equation (10) and backwardly propagated (marked with the left-facing arrow in Figure 2) for updating the parameter values.…”

Section: Training the Convolutional Neural Network On A Gpu Worktationmentioning

confidence: 99%

“…Verbandt et al 9 designed a landmark consisting of a series of concentric circles with exponentially distributed radii (Figure 1 (d)). Jung et al 10 designed an H-shaped landmark with concentric circles (Figure 1(e)). These landmarks are designed to contain sharp or contrastive features that are easy to identify and segment from the background.…”

Section: Introductionmentioning

confidence: 99%

“…Jung et al. 10 designed an H-shaped landmark with concentric circles (Figure 1(e)). These landmarks are designed to contain sharp or contrastive features that are easy to identify and segment from the background.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for vision-based micro aerial vehicle autonomous landing

Luo

et al. 2018

International Journal of Micro Air Vehicles

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Vision-based techniques are widely used in micro aerial vehicle autonomous landing systems. Existing vision-based autonomous landing schemes tend to detect specific landing landmarks by identifying their straightforward visual features such as shapes and colors. Though efficient to compute, these schemes only apply to landmarks with limited variability and require strict environmental conditions such as consistent lighting. To overcome these limitations, we propose an end-to-end landmark detection system based on a deep convolutional neural network, which not only easily scales up to a larger number of various landmarks but also exhibit robustness to different lighting conditions. Furthermore, we propose a separative implementation strategy which conducts convolutional neural network training and detection on different hardware platforms separately, i.e. a graphics processing unit work station and a micro aerial vehicle on-board system, subject to their specific implementation requirements. To evaluate the performance of our framework, we test it on synthesized scenarios and real-world videos captured by a quadrotor on-board camera. Experimental results validate that the proposed vision-based autonomous landing system is robust to landmark variability in different backgrounds and lighting situations.

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Section: Training the Convolutional Neural Network On A Gpu Worktationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for vision-based micro aerial vehicle autonomous landing

Luo

et al. 2018

International Journal of Micro Air Vehicles

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“…Vision-based techniques, as in [ 18 ], to estimate marker locations, intercepted UAV states, or landing platform location are quite commonly used, especially with attention paid to robustifying approaches, as in [ 19 ]. The approaches to the landing problem, available in the literature, as in [ 20 , 21 , 22 ] or [ 23 ], do not focus on optimization of the landing task itself, but on building control laws to land the UAV in the autonomous mode based on, e.g., detected markers. Autonomous outdoor landing procedures using static marker tracking are proposed in [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Altitude Measurement-Based Optimization of the Landing Process of UAVs

Horla

Giernacki

Cieślak

et al. 2021

Sensors

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The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach.

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“…This paper relies on computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation. Authors in [4,5] propose drone landing technology by identifying a sign and then landing the drone on the marker. Therefore, the drone needs to place the landing mark in the landing area before landfall.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

Yang

Zhang

et al. 2018

Electronics

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Abstract:With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. When GPS signals are missing, these drones cannot land or recover properly. In fact, with the help of optical equipment and image recognition technology, the position and posture of the drone in three dimensions can be obtained, and the environment where the drone is located can be perceived. This paper proposes and implements a monocular vision-based drone autonomous landing system in emergencies and in unstructured environments. In this system, a novel map representation approach is proposed that combines three-dimensional features and a mid-pass filter to remove noise and construct a grid map with different heights. In addition, a region segmentation is presented to detect the edges of different-height grid areas for the sake of improving the speed and accuracy of the subsequent landing area selection. As a visual landing technology, this paper evaluates the proposed algorithm in two tasks: scene reconstruction integrity and landing location security. In these tasks, firstly, a drone scans the scene and acquires key frames in the monocular visual simultaneous localization and mapping (SLAM) system in order to estimate the pose of the drone and to create a three-dimensional point cloud map. Then, the filtered three-dimensional point cloud map is converted into a grid map. The grid map is further divided into different regions to select the appropriate landing zone. Thus, it can carry out autonomous route planning. Finally, when it stops upon the landing field, it will start the descent mode near the landing area. Experiments in multiple sets of real scenes show that the environmental awareness and the landing area selection have high robustness and real-time performance.

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Close-range vision navigation and guidance for rotary UAV autonomous landing

Cited by 13 publications

References 16 publications

Deep learning for vision-based micro aerial vehicle autonomous landing

Deep learning for vision-based micro aerial vehicle autonomous landing

Altitude Measurement-Based Optimization of the Landing Process of UAVs

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

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