This paper presents a solution for false matching detection in scene matching-based aerial navigation systems. A navigation system that uses normalized cross-correlation to match a captured image with a reference image was designed. While traditional methods rely on statistical indicators to detect false matchings, this research relied on deep learning using Convolutional Neural Network (CNN). A CNN was trained to online predict the probability of a matching result to be true or false. The training dataset of images was constructed depending on the knowledge of where good matching areas are expected to be. The probability numbers were stored as an assistant map to be used again with the same reference map without classification. The system was implemented and tested in a 3D simulation environment using models for a drone, camera, and flight environment. The Robot Operating System (ROS) and the 3D dynamic simulator Gazebo were used for simulation. The results proved the efficiency of the proposed method in excluding the false matchings. Using the assistant map without classification resulted in an execution time of 41ms and RMS error of position less than 1.2m.
Single camera-based navigation systems need information from other sensors or from the work environment to produce reliable and accurate position measurements. Providing such trustable, accurate, and available information in the environment is very important. The work highlights that the availability of well-described streets in urban environments can be exploited by drones for navigation and path tracking purposes, thus benefitting from such structures is not limited to only automated driving cars. While the drone position is continuously computed using visual odometry, scene matching is used to correct the position drift depending on some landmarks. The drone path is defined by several waypoints, and landmarks centralized by those waypoints are carefully chosen in the street intersections. The known streets’ geometry and dimensions are used to estimate the image scale and orientation which are necessary for images alignment, to compensate for the visual odometry drift, and to pass closer to the landmark center by the visual servoing process. Probabilistic Hough transform is used to detect and extract the street borders. The system is realized in a simulation environment consisting of the Robot Operating System ROS, 3D dynamic simulator Gazebo, and IRIS drone model. The results prove the suggested system efficiency with a 1.4 m position RMS error.
This paper presents the implementation of a mutual-aided navigation system for an aerial vehicle. Employing all available sensors in navigation is effective at maintaining continuous and optimal results. The images offer a lot of information about the surrounding environment, but image processing is time-consuming and causes timing problems. While traditional fusion algorithms tend to reduce the delay errors or ignore them, this research depends on state estimation recalculation during the delay time and on sequential filtering. To reduce the image matching time, the map is processed offline, then key point clusters are stored to avoid feature recalculation online. The sensors’ information is used to bound the search space for the matched features on the map, then they are reprojected on the captured images to exclude the unuseful part from processing. The suggested mutual-aided form compensates for the inertial system drift, which enhances the system’s accuracy and independence. The system was tested using data collected from a real flight using a DJI drone. The measurements from an inertial measurement unit (IMU), camera, barometer, and magnetometer were fused using a sequential Kalman Filter. The final results prove the efficiency of the suggested system to navigate with high independency, with an RMS position error of less than 3.5 m.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.