An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC.

The Global Positioning System (GPS) is interfered easily, for the need of Unmanned Aerial Vehicle (UAV)'s landing automatically and accurately in all weather, a new program of UAV's automatic landing is proposed based on the cooperative infrared object on the runway and the infrared computer vision in UAV. The cooperative object is recognized by affine moment invariants that is robust to geometric deformation from different big bevel angle of view in distant place. As there are some distortions of camera lens, then we used the characteristic points from the image of the cooperative infrared object to compute UAV's the angle of pitching, the angle of roll and the angle of yaw relative to the runway by Tsai calibration method that can rectify camera lens distortions. The experimental result shows that the average correct identification rate reaches 97%, better than the two prevenient algorithms based on the direction code and the traditional moment invariants, the time consumption of this new recognition algorithm is only 25% of the two algorithms. Simulations show that the method of pose estimation in this paper is full-informationed, accurate and feasible for the pose estimation of the UAV comparing with the results of references.

Unmanned aerial vehicle (UAV) photogrammetry is a technology used to create highly accurate maps, and the real-time kinematic (RTK) global navigation satellite system (GNSS) network has been implemented by several organizations to support their missions.Recent advancements in computer vision have driven progress in photogrammetry, alongside the development of more accessible UAVs equipped with GNSS technology.This paper evaluates the accuracy of UAV imagery and processing using ground control points (GCPs) with coordinates from the Thailand RTK GNSS network.The study area is located on the Sathing-Phra Peninsula in Songkhla Province, southern Thailand, covering approximately 174 hectares with a total of 660 images.The average ground sampling distance (GSD) was around 4.35 cm.The processed photogrammetry system used 8 GCPs via the GNSS RTK network, and results were compared with 25 checkpoints from a static survey.The UAV photogrammetry results showed a horizontal accuracy for orthomosaics and vertical accuracy for the digital surface model (DSM), with CE 95 values of 6.99 cm and 12.44 cm, respectively, as per the NSSDA standard.These results comply with the Thailand UAV Surveying for Engineering (TUSE) standards.According to the 2014 American Society for Photogrammetry and Remote Sensing (ASPRS) standards, horizontal accuracy class I and vertical accuracy class II should be applied.The map scale factor (MSF) standard was 1:160, with a contour interval of 20 cm.Additionally, the orthomosaic DSM can be imported into open-source programs for use in a web GIS online system, enabling local agencies to retrieve and utilize the data.

Autonomous aerial refueling (AAR) is an important capability for an unmanned aerial vehicle (UAV) to increase its flying range and endurance without increasing its size. This paper presents a novel tracking method that utilizes both 2D intensity and 3D point-cloud data acquired with a 3D Flash LIDAR sensor to establish relative position and orientation between the receiver vehicle and drogue during an aerial refueling process. Unlike classic, vision-based sensors, a 3D Flash LIDAR sensor can provide 3D point-cloud data in real time without motion blur, in the day or night, and is capable of imaging through fog and clouds. The proposed method segments out the drogue through 2D analysis and estimates the center of the drogue from 3D point-cloud data for flight trajectory determination. A level-set front propagation routine is first employed to identify the target of interest and establish its silhouette information. Sufficient domain knowledge, such as the size of the drogue and the expected operable distance, is integrated into our approach to quickly eliminate unlikely target candidates. A statistical analysis along with a random sample consensus (RANSAC) is performed on the target to reduce noise and estimate the center of the drogue after all 3D points on the drogue are identified. The estimated center and drogue silhouette serve as the seed points to efficiently locate the target in the next frame.

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

Hierarchical motion consistency constraint for efficient geometrical verification in UAV stereo image matching

PPP-based UAV modeling using Galileo HAS orbit/clock products and RTS smoother

Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods

This work describes the methodology for detecting pipes and their pose in refineries inspection using Unmanned Aerial Vehicles (UAV s) for remote Ultrasonic Testing (UT). Segmentation techniques such as the Hough Transform and its variations, and Random Sample Consensus have been widely used. This paper is therefore focused on the development of an efficient computer vision algorithm to detect the position and orientation of the pipes in order to land on them autonomously to perform the inspection, by using 3D point cloud information from depth cameras. Applying a methodology based on Random Sample Consensus and point cloud pre-processing to fasten the algorithm performance has led to robust estimations of the pipes and their poses in an indoor testbed using a realistic environment, allowing the autonomous landing and the subsequent inspection.

Abstract. In the fields of mapping, surveying, and three-dimensional (3D) modeling, unmanned aerial vehicle (UAV) systems have become essential equipment in recent times. UAVs are increasingly widely used, especially in the field of photogrammetry. The integration of technologies such as digital cameras, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS) has improved the quality and accuracy of photogrammetric products. In comparison to conventional methodologies, UAVs present a cost-effective alternative to traditional aerial photogrammetry. Recent advancements in technology substantiate their efficacy across a variety of applications. The utilization of direct georeferencing minimizes the reliance on ground control points (GCP). With the advancements in positioning techniques, the accuracy of the products is concurrently improving. Real-Time Kinematic (RTK) and Post-Processed Kinematic (PPK) techniques play a critical role in enhancing the accuracy of UAV-based photogrammetry. Both techniques are widely used in UAV-based photogrammetry. However, relative positioning poses limitations to the study in terms of infrastructure and cost. PPP (Precise Point Positioning), a specific form of absolute positioning, is utilized effectively across various applications. The PPP method, used globally with a single GNSS receiver, is more cost-effective than relative methods. In this study, the performance of the PPP-AR technique as an alternative to the relative methods RTK and PPK is evaluated. Thus, the applicability of the PPP technique, which has improved accuracy and precision with ambiguity resolution, in UAV-based photogrammetry is examined. RTK and PPK techniques achieve Root Mean Square Error (RMSE) values of less than 10 centimeters. In contrast, RMSE values at the decimeter level are observed in the PPP-AR technique.

The approach presented in this paper provides real-time estimation of a gaseous plume concentration using an unmanned aerial vehicle with sensors. The plume is generated by a moving aerial source and is modeled by the unsteady three-dimensional advection–diffusion equation with ambient winds and eddy diffusivities. The approach couples the unmanned aerial vehicle guidance with the performance of the concentration estimator and is implemented with efficient computational methods for real-time estimation. The unmanned aerial vehicle is modeled as a point-mass fixed-wing aircraft resulting in a sixth-order nonlinear dynamical system. The unmanned aerial vehicle is guided toward the region of maximum state-estimation error via a Lyapunov-redesign method. The estimation scheme provides the desired Cartesian velocities of the unmanned aerial vehicle, and a lower-level controller provides the control signals to the unmanned aerial vehicle. The finite volume discretization of the estimator incorporates a second-order total-variation-diminishing scheme for the advection term and dynamic grid adaptation with local grid-refinement centered at the unmanned aerial vehicle location. The approach is applied to a stationary source and a moving aerial source using realistic unmanned aerial vehicle specifications. The estimated plumes are compared with simulated plume data and show the effectiveness of the approach.

Aerial photogrammetry by Unmanned Aerial Vehicles (UAVs) is a widespread method to perform mapping tasks with high-resolution to reconstruct three-dimensional (3D) building and façade models. However, the survey of Ground Control Points (GCPs) represents a time-consuming task, while the use of Real-Time Kinematic (RTK) drones allows for one to collect camera locations with an accuracy of a few centimeters. DJI Phantom 4 RTK (DJI-P4RTK) combines this with the possibility to acquire oblique images in stationary conditions and it currently represents a versatile drone widely used from professional users together with commercial Structure-from-Motion software, such as Agisoft Metashape. In this work, we analyze the architectural application of this drone to the photogrammetric modeling of a building with particular regard to metric survey specifications for cultural heritage for 1:20, 1:50, 1:100, and 1:200 scales. In particular, we designed an accuracy assessment test signalizing 109 points, surveying them with total station and adjusting the measurements through a network approach in order to achieve millimeter-level accuracy. Image datasets with a designed Ground Sample Distance (GSD) of 2 mm were acquired in Network RTK (NRTK) and RTK modes in manual piloting and processed both as single façades (S–F) and as an overall block (4–F). Subsequently, we compared the results of photogrammetric models generated in Agisoft Metashape to the Signalized Point (SP) coordinates. The results highlight the importance of processing an overall photogrammetric block, especially whenever part of camera locations exhibited a poorer accuracy due to multipath effects. No significant differences were found between the results of network real-time kinematic (NRTK) and real-time kinematic (RTK) datasets. Horizontal residuals were generally comparable to GNSS accuracy in NRTK/RTK mode, while vertical residuals were found to be affected by an offset of about 5 cm. We introduced an external GCP or used one SP per façade as GCP, assuming a poorer camera location accuracy at the same time, in order to fix this issue and comply with metric survey specifications for the widest architectural scale range. Finally, both S–F and 4–F projects satisfied the metric survey requirements of a scale of 1:50 in at least one of the approaches tested.

Unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance mission. In this paper, we present a systematic approach for the generation of UAV trajectory using a video image matching system based on SURF (Speeded up Robust Feature) and Preemptive RANSAC (Random Sample Consensus). Video image matching to find matching points is one of the most important steps for the accurate generation of UAV trajectory (sequence of poses in 3D space). We used the SURF algorithm to find the matching points between video image sequences, and removed mismatching by using the Preemptive RANSAC which divides all matching points to outliers and inliers. The inliers are only used to determine the epipolar geometry for estimating the relative pose (rotation and translation) between image sequences. Experimental results from simulated video image sequences showed that our approach has a good potential to be applied to the automatic geo-localization of the UAVs system

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

Quantitative structural analysis is a useful approach for studying geologic structures. It is particularly important in remote and complex fold-thrust belts where outcrop data and high-quality seismic reflection images are challenging to obtain. In this study, we integrated terrestrial light detection and ranging (LiDAR), unmanned aerial vehicle (UAV), and Landsat 8 (L8) data to extract high-resolution topographic and surface geologic information and constrain interpretations of three-dimensional (3-D) seismic reflection data in the Hero Range of the Qaidam Basin (QB) in northwestern China. UAV images were used to obtain a digital elevation model (DEM) and to measure the orientation of sedimentary bedding. Terrestrial LiDAR data were used to generate high-resolution digital outcrops and to evaluate the accuracy of the UAV-based DEM. L8 images were used to distinguish different stratigraphic units. The random sample consensus (RANSAC) algorithm was adopted to ascertain the best-fit plane of bedding. The results show that UAV images can be used to construct a DEM with <;1 m resolution and orthophotos with 0.15-m resolution. Collectively, these data improve the ability to identify and measure small exposures of bedding surfaces. The RANSAC algorithm improves the accuracy of measuring bedding orientations by removing erroneous selection points and facilitating the recognition of second-order variations in bedding orientation. The integrated analysis of remotely sensed and 3-D seismic data indicates that, of the three anticlines within the Hero Range, two are fault-propagation folds (the Shizigou and Youshashan anticlines) and one is associated with a pop-up structure (Ganchaigou anticline).

The paper treats a problem of building an image mosaic or a 2D map from the sequence of images taken by an Unmanned Aerial Vehicle (UAV). The proposed algorithm is composed of the following parts: feature detection and extraction using Speeded Up Robust Features (SURF) descriptor, feature matching using Fast Approximate Nearest Neighbor Search (FANN) with automatic algorithm configuration, estimation of the optimal transform between images using RANdom SAmple Consensus (RANSAC) and finally creating a mosaic by stitching images together using the obtained transformations. Summarizing results obtained in two different scenarios, it can be concluded that the proposed algorithm exhibits near real time performance for automated UAV 2D aerial mapping.