Abstract
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.
Highlights
In the past decades, unmanned aerial vehicles (UAVs) have been widely used in many fields
Most attention is generally paid on fixed-wing aerial vehicle recovery because of relatively higher risk involved during the landing phase
The success of flying aircraft navigation is mostly achieved by using onboard conventional sensors, such as global positioning system (GPS), inertial measurement unit (IMU) and magnetometer
Summary
In the past decades, unmanned aerial vehicles (UAVs) have been widely used in many fields. Most attention is generally paid on fixed-wing aerial vehicle recovery because of relatively higher risk involved during the landing phase. Many practical applications showed that recovery is the most challenging and hazardous period of UAV flights [2]. Developing autonomous landing technologies has already been an important trend of runway-mode takeoff-and-landing UAV systems. It aims at reducing personnel dependency and workload and improving adaptability and reliability of flying vehicles recovery. The success of flying aircraft navigation is mostly achieved by using onboard conventional sensors, such as global positioning system (GPS), inertial measurement unit (IMU) and magnetometer. Autonomous landing task that requires higher accuracy in localization is still not achievable solely by these onboard sensors [3, 4]
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