A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Yuan, Haiwen; Xiao, Changshi; Xiu, Supu; Zhan, Wenqiang; Ye, Zhenyi; Zhang, Fan; Zhou, Chunhui; Wen, Yuanqiao; Li, Qiliang

doi:10.1177/1550147718800655

Cited by 4 publications

(3 citation statements)

References 34 publications

(34 reference statements)

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“…A plethora of different autonomous landing approaches is discussed in [17]. The existing solutions pertaining to outdoor landing, which we will focus upon, can mainly be classified into "known" [18][19][20][21] and "unknown" environments [22]. The landing task can be broken down into two main phases: (1) finding an appropriate landing spot and (2) executing the landing maneuver.…”

Section: Related Literaturementioning

confidence: 99%

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

et al. 2022

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In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the drone’s camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV.

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Section: Related Literaturementioning

confidence: 99%

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

et al. 2022

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“…Vision-based autonomous landing has used landing markers in an "H"-shape pattern; landing markers inspired by QR code [2]; ArUco markers [3], AprilTag markers [4]; and special-pattern black and white markers [5] and color markers [6]. An onboard implementation of the computer vision algorithms to detect a moving platform has been demonstrated using CPU [7] and GPU [8].…”

Section: State Of the Artmentioning

confidence: 99%

Energy-Aware Design of Vision-Based Autonomous Tracking and Landing of a UAV

Zamanakos

Seewald

Midtiby

et al. 2020

2020 Fourth IEEE International Conference on Robotic Computing (IRC)

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In this paper, we present the design and evaluation of a vision-based algorithm for autonomous tracking and landing on a moving platform in varying environmental conditions. We use an energy-aware approach, where the design of the algorithm is based on an evaluation of the energy consumption and Quality of Service (QoS) of each computational component. We evaluate our approach with an agricultural use case where a moving platform is tracked using a landing marker and the YOLOv3tiny CNN is used to detect ground-based hazards. We perform all computations onboard using an NVIDIA Jetson Nano and analyse the impact on the flight time by profiling the energy consumption of the marker detection and the CNN. Experiments are conducted in Gazebo simulation using an energy modeling tool to measure the computational energy cost as a function of QoS. We test the energy efficiency and robustness of our system in various dynamic wind disturbances. We show that the marker detection algorithm can be run at the highest QoS with only a marginal energy overhead whereas adapting the QoS level of CNN results in a considerable power saving. The power saving is significant for a system executing on a fixed-wing UAV.

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“…Then, RANSAC is used to stitch the fine line segments to detect the whole horizon line. To solve this problem, some researchers attempt to utilize the unmanned aerial vehicle (UAV) to enhance the visual perception ability of the USV [25,26], which increases the complexity of the unmanned system.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation

et al. 2020

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The intelligentization of unmanned surface vehicles (USVs) has recently attracted intensive interest. Visual perception of the water scenes is critical for the autonomous navigation of USVs. In this paper, an adaptive semantic segmentation method is proposed to recognize the water scenes. A semantic segmentation network model is designed to classify each pixel of an image into water, land or sky. The segmentation result is refined by the conditional random field (CRF) method. It is further improved accordingly by referring to the superpixel map. A weight map is generated based on the prediction confidence. The network trains itself with the refined pseudo label and the weight map. A set of experiments were designed to evaluate the proposed method. The experimental results show that the proposed method exhibits excellent performance with few-shot learning and is quite adaptable to a new environment, very efficient for limited manual labeled data utilization.

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A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Cited by 4 publications

References 34 publications

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

Energy-Aware Design of Vision-Based Autonomous Tracking and Landing of a UAV

Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation

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