Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach

Ma, Zhiliang; Hu, Tianjiang

doi:10.5772/62257

Cited by 27 publications

(16 citation statements)

References 21 publications

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“…This airspeed is generated by the forward thrust produced usually by a propeller turned by an electric motor. It is mainly characterized by its high cruise speed and long endurance and is mostly used for long distance, long range and high altitude missions (e.g., [38][39][40][41]). Two models of fixed-wing UAVs are illustrated in Figure 5 (see a.1 and a.2).…”

Section: Class Of Uavmentioning

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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Section: Class Of Uavmentioning

confidence: 99%

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

2019

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“…The combination of the vision and IMU data reported in [21,22] assumed that the IMU had the ability to provide a good roll and pitch attitude estimation, and four infrared spots on the target or the landing spot could be detected using the vision system. In addition, stereo vision using triangulation has been applied during a UAV autonomous landing [23,24].…”

Section: Previous Workmentioning

confidence: 99%

A Hierarchical Vision-Based UAV Localization for an Open Landing

et al. 2018

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The localization of unmanned aerial vehicles (UAVs) for autonomous landing is challenging because the relative positions of the landing objects are almost inaccessible and the objects have nearly no transmission with UAVs. In this paper, a hierarchical vision-based localization framework for rotor UAVs is proposed for an open landing. In such a hierarchical framework, the landing is defined into three phases: "Approaching", "Adjustment", and "Touchdown". Object features at different scales can be extracted from a designed Robust and Quick Response Landing Pattern (RQRLP) and the corresponding detection and localization methods are introduced for the three phases. Then a federated Extended Kalman Filter (EKF) structure is costumed and utilizes the solutions of the three phases as independent measurements to estimate the pose of the vehicle. The framework can be used to integrate the vision solutions and enables the estimation to be smooth and robust. In the end, several typical field experiments have been carried out to verify the proposed hierarchical vision framework. It can be seen that a wider localization range can be extended by the proposed framework while the precision is ensured.

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“…Existing studies on vision-based landing for fixed-wing aircraft are classified into two categories, namely ground-based and onboard-based. The ground-based methods [4,5,6,7,8,9,10] often utilize multilocular vision systems arranged on the ground to detect, track, and guide the aircraft to landing. Martínez et al [4] designed a trinocular system, which is composed of three Firewire cameras fixed on the ground, to estimate an UAV’s position and orientation by tracking color land markers on the UAV.…”

Section: Introductionmentioning

confidence: 99%

“…Martínez et al [4] designed a trinocular system, which is composed of three Firewire cameras fixed on the ground, to estimate an UAV’s position and orientation by tracking color land markers on the UAV. Kong et al [5,6,7] developed a custom-built infrared stereo camera with a large field of view and claimed that their system could resist all weather conditions, and further improve the detection precision by the Chan-Vese method [8] and the saliency-inspired method [9]. In addition, Yang et al [10] showed promising results for UAV auto-landing in GPS-denied environments using a ground-based infrared camera array and a near infrared laser lamp-based cooperative optical imaging method.…”

Section: Introductionmentioning

confidence: 99%

Infrared-Inertial Navigation for Commercial Aircraft Precision Landing in Low Visibility and GPS-Denied Environments

Zhang

Zhai²,

He³

et al. 2019

Sensors

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This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments.

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Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach

Cited by 27 publications

References 21 publications

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

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

A Hierarchical Vision-Based UAV Localization for an Open Landing

Infrared-Inertial Navigation for Commercial Aircraft Precision Landing in Low Visibility and GPS-Denied Environments

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