Multi-resolution visual fiducial and assistant navigation system for unmanned aerial vehicle landing

Yu, Chong; Cai, Jiyuan; Chen, Qingyu

doi:10.1016/j.ast.2017.03.008

Cited by 18 publications

(16 citation statements)

References 4 publications

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“…We already know the pixel size (see Section 2) of the tracking camera, so we can calculate the distance between the center of target and the center of the mos plane on the camera mos plane. The equations are shown in (1) and (2).…”

Section: Motion Control Model Based On Angular Speed For Pan-tilt Plamentioning

confidence: 99%

“…Initialization particle tracking algorithm with the target (2) While true do (3) Find the position of the target on frame −1 , with particle tracking algorithm (4)…”

Section: Mathematical Model Of Motion Controlmentioning

confidence: 99%

“…In recent years, visual object tracking has been used in many fields such as video surveillance [1], unmanned assistance systems [2], and autonomous robotics [3]. Visual object tracking with pan-tilt platform can be employed as active surveillance, trajectory measured, and so on.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Motion‐Intelligence‐Based Control Algorithm for Object Tracking by Controlling PAN‐Tilt Automatically

Yang

Tang

Pei

et al. 2019

Mathematical Problems in Engineering

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As computer vision develops, pan-tilt platform visual systems are able to track moving target over static camera systems. In this paper, a novel motion-intelligence-based control algorithm for object tracking by controlling pan-tilt platform has been proposed. The algorithm includes the motion control model based on angular speed and the intelligent control algorithm based on reinforcement learning (RL). The motion control model converts deviation between the center point of the tracked target and the center point of the frame to angular speed of pan-tilt platform. It can keep position of the tracked object in the center of the frame automatically. The intelligent control algorithm based on reinforcement learning can reduce the error between the ideal value and the actual value when the pan-tilt platform moves. The two blocks work together to make the pan-tilt platform track a dynamic object more stably and the experiment result shows that both the tracking accuracy and robustness are improved.

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Section: Motion Control Model Based On Angular Speed For Pan-tilt Plamentioning

confidence: 99%

“…Initialization particle tracking algorithm with the target (2) While true do (3) Find the position of the target on frame −1 , with particle tracking algorithm (4)…”

Section: Mathematical Model Of Motion Controlmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Motion‐Intelligence‐Based Control Algorithm for Object Tracking by Controlling PAN‐Tilt Automatically

Yang

Tang

Pei

et al. 2019

Mathematical Problems in Engineering

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show abstract

“…Researchers in [14] intended to land the quadrotor while it was aligned with the marker. In some other studies, the automatic landing was simulated in the Gazebo simulation environment [15][16][17]. Furthermore, load transporting problem in UAVs was investigated using neural networks in [18], where the take-off, landing, and target tracking missions were all carried out autonomously in real time.…”

Section: Introductionmentioning

confidence: 99%

Automatic landing of a low-cost quadrotor using monocular vision and Kalmanfilter in GPS-denied environments

Sani¹,

Shoaran²,

Karimian³

2019

Turk J Elec Eng & Comp Sci

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Unmanned aerial vehicles are becoming an important part of the modern life. Despite some recent advances in GPS-aided navigation of quadrotors, the concern of crash and collision still overshadows their reliability and safety, especially in GPS-denied environments. Therefore, the necessity for developing fully automatic methods for safe, accurate, and independent landing of drones increases over time. This paper investigates the autolanding process by focusing on an accurate and continuous position estimation of the drone using a monocular vision system and the fusion with the inertial measurement unit and ultrasonic sensors' data. An ARUCO marker is used as the landing pad, and the information is processed in the ground station through a real-time Wi-Fi link. In order to overcome the closed loop instability caused by the communication and localization delays, we propose a method called "movement slicing method". This method divides the moves around the marker into moving and waiting slices and makes the landing process not only more accurate but also faster. Experimental results show a successful landing of the UAV on a predefined location, while it is accurately aligned with the marker using the proposed method.

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“…Here, the cameras are separate or have different orientations (e.g., [60][61][62]). A multi-camera configuration is illustrated in Figure 6 (a.2).…”

mentioning

confidence: 99%

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019

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Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

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Multi-resolution visual fiducial and assistant navigation system for unmanned aerial vehicle landing

Cited by 18 publications

References 4 publications

A Novel Motion‐Intelligence‐Based Control Algorithm for Object Tracking by Controlling PAN‐Tilt Automatically

A Novel Motion‐Intelligence‐Based Control Algorithm for Object Tracking by Controlling PAN‐Tilt Automatically

Automatic landing of a low-cost quadrotor using monocular vision and Kalmanfilter in GPS-denied environments

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

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