Weiwei Kong

Autonomous landing of an UAV with a ground-based actuated infrared stereo vision system

In this study, we focus on the problem of landing an unmanned aerial vehicle (UAV) in unknown and Global Navigation Satellite System(GNSS)-denied environments based on an infrared stereo vision system. This system is fixed on the ground and used to track the UAVs position during the landing process. In order to enlarge the search field of view (FOV), a pan-tilt unit (PTU) is employed to actuate the vision system. The infrared camera is chosen as the exteroceptive sensor for two main reasons: first, it can be used under all weather conditions and around the clock; second, infrared targets can be tracked based on infrared spectrum features at a lower computational cost compared to tracking texture features in visible spectrum. State-of-the-art active contour based algorithms and the mean shift algorithm have been evaluated with regard to detecting and tracking an infrared target. Field experiments have been carried out using an unmanned quadrotor and a fixed-wing unmanned aircraft, with both qualitative and quantitative evaluations. The results demonstrate that our system can track UAVs without artificial markers and is sufficient to enhance or replace the GNSS-based localization in GNSS-denied environment or where its information is inaccurate.

Vision-based autonomous landing system for unmanned aerial vehicle: A survey

Recently, there has been growing interest in developing unmanned aircraft system (UAS) based on visual sensors. During the whole autonomous assignment, the landing procedure is one of the most dangerous and challenging process. For most of unmanned aircraft vehicle, visual sensors are the basic equipment, which are also widely used during the landing maneuver. This paper first presents the main research groups involved in the development of vision-based autonomous landing systems. Then it discusses the detail of each algorithms and systems in different categories. The goal of this paper is to review the state-of-the-art vision-based autonomous landing methods that captures all milestones and seminal works. These algorithms and systems are classified into different categories. Finally, the paper highlights challenges in this research field.

A ground-based optical system for autonomous landing of a fixed wing UAV

This paper presents a new ground-based visual approach for guidance and safe landing of an unmanned aerial vehicle (UAV) in Global Navigation Satellite System(GNSS)-denied environments. In our previous work, the old system consists of one pan-tilt unit(PTU) with two cameras, whose detection range is limited by the baseline. To achieve long-range detection and cover wide field of regard, we mounted two separate sets of PTU integrated with visible light camera on both sides of the runway instead of our previous assembled stereo vision system. Then, the well-known AdaBoost method was evaluated with regard to detecting and tracking the target. To achieve the relative position between the UAV and landing area, we used triangulation to calculate the 3D coordinates of the UAV. By combining the estimated position in the closed loop control, we obtain the autonomous landing strategy. Finally, we present several real flights in outdoor environments, and compare its accuracy with ground truth provided by GNSS. The results support the validity and accuracy of the presented system.

Ground Stereo Vision-Based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach

This article aims at flying target detection and localization of a fixed-wing unmanned aerial vehicle (UAV) autonomous take-off and landing within Global Navigation Satellite System (GNSS)-denied environments. A Chan-Vese model–based approach is proposed and developed for ground stereo vision detection. Extended Kalman Filter (EKF) is fused into state estimation to reduce the localization inaccuracy caused by measurement errors of object detection and Pan-Tilt unit (PTU) attitudes. Furthermore, the region-of-interest (ROI) setting up is conducted to improve the real-time capability. The present work contributes to real-time, accurate and robust features, compared with our previous works. Both offline and online experimental results validate the effectiveness and better performances of the proposed method against the traditional triangulation-based localization algorithm.

Localization Framework for Real-Time UAV Autonomous Landing: An On-Ground Deployed Visual Approach

One of the greatest challenges for fixed-wing unmanned aircraft vehicles (UAVs) is safe landing. Hereafter, an on-ground deployed visual approach is developed in this paper. This approach is definitely suitable for landing within the global navigation satellite system (GNSS)-denied environments. As for applications, the deployed guidance system makes full use of the ground computing resource and feedbacks the aircraft’s real-time localization to its on-board autopilot. Under such circumstances, a separate long baseline stereo architecture is proposed to possess an extendable baseline and wide-angle field of view (FOV) against the traditional fixed baseline schemes. Furthermore, accuracy evaluation of the new type of architecture is conducted by theoretical modeling and computational analysis. Dataset-driven experimental results demonstrate the feasibility and effectiveness of the developed approach.

A ground-based multi-sensor system for autonomous landing of a fixed wing UAV

This paper presents a novel ground-based multi-sensor system for guidance and safe landing of a fixed-wing Unmanned Aerial Vehicle (UAV). Unlike with our previous works, this newly developed system is based on one Pan/Tilt Unit (PTU) with visible light camera laser pointer and Ultra-wideband (UWB) radar. To simplify the calibration process in the wide area, a DGPS-based calibration method was designed. As a compensation for camera, we integrated UWB radar module to achieve higher accuracy of localization and implemented laser pointer with retroreflector to enhance the target feature in the distance, which extends the detection range. Then, we used Canny edge detector and Hough transform to track the UAV in the air. Finally, several field experiments with small- and middle-size platforms demonstrate that our system has the ability to acquire the target from a long range and meet the requirements of robustness and real-time capability. Therefore, the proposed system is suitable to complement or replace the GNSS-based position estimation in situations where GNSS information is unavailable or inaccurate.

ROS-based ground stereo vision detection: implementation and experiments.

This article concentrates on open-source implementation on flying object detection in cluttered scenes. It is of significance for ground stereo-aided autonomous landing of unmanned aerial vehicles. The ground stereo vision guidance system is presented with details on system architecture and workflow. The Chan–Vese detection algorithm is further considered and implemented in the robot operating systems (ROS) environment. A data-driven interactive scheme is developed to collect datasets for parameter tuning and performance evaluating. The flying vehicle outdoor experiments capture the stereo sequential images dataset and record the simultaneous data from pan-and-tilt unit, onboard sensors and differential GPS. Experimental results by using the collected dataset validate the effectiveness of the published ROS-based detection algorithm.