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Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude. [ABSTRACT FROM AUTHOR]

Over the last three decades, satellite imagery has been instrumental in mapping and monitoring water quality. However, satellites often have limitations due to image availability and cloud cover. Today, the spatial resolution of satellite images does not provide finer detail measurements essential for small‐scale water pollution management. Drones offer a complimentary platform capable of operating below cloud cover and acquiring very high spatial resolution datasets in near real‐time. Studies have shown that drone mapping over water can be done via the Direct Georeferencing approach. However, this method is only suitable for high‐end drones with accurate GNSS/IMU. Importantly, this limitation is exacerbated because of the difficulty in placing targets over water, which can be used to improve the accuracy after the survey. This study explored a new method called Assisted Direct Georeferencing which combines the benefits of traditional Bundle Adjustment with Direct Georeferencing. The performance of the approach was evaluated over a variety of different scenarios, demonstrating significant improvement in the planimetric accuracy. From the results, the method reduced the error in XY of drone imagery from MAE of 18.9 to 3.4 m. The result shows the potential of low‐cost drones with Assisted Direct Georeferencing in closing the gap to high‐end drones. [ABSTRACT FROM AUTHOR]

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use. [ABSTRACT FROM AUTHOR]

This paper presents a calibration approach for multiple synchronized global-shutter RGB cameras surrounding a large capture volume for 3D application. The calibration approach uses an active wand with two LED-embedded markers waved manually within the target capture volume. Data from the waving wand are combined with chessboard images taken at close range during each camera's intrinsic calibration, optimizing camera parameters via our proposed bundle adjustment method. These additional constraints from the chessboard are developed to overcome an overfitting issue of wand-based calibration discovered by benchmarking its 3D triangulation accuracy in an independent record against a ground-truth trajectory and not on the record used for calibration itself. Addressing this overfitting issue in bundle adjustment leads to significant improvements in both 3D accuracy and result consistency. As a by-product of this development, a new benchmarking workflow and our calibration dataset that reflects realistic 3D accuracy are proposed and made publicly available to allow for fair comparisons of various calibration methods in the future. Additionally, our experiment highlights a significant benefit of a ray distance-based (RDB) triangulation formula over the popular direct linear transformation (DLT) method. [ABSTRACT FROM AUTHOR]

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera's attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recent advancements in satellite technology have significantly increased the availability of high-resolution imagery for Earth observation, enabling nearly all regions to be captured frequently throughout the year. These images have become a vast source of big data and hold immense potential for various applications, including environmental monitoring, urban planning, and disaster management. However, obtaining ground control points (GCPs) and performing geometric correction is a time-consuming and costly process, often limiting the efficient use of these images. To address this challenge, this study introduces a Rational Function Model (RFM)-based rigorous bundle adjustment method to enhance the relative geometric positioning accuracy of multiple KOMPSAT-3A images without the need for GCPs. The proposed method was tested using KOMPSAT-3A images. The results showed a significant improvement in geometric accuracy, with mean positional errors reduced from 30.02 pixels to 2.21 pixels. This enhancement ensured that the corrected images derived from the proposed method were reliable and accurate, making it highly valuable for various geospatial applications. [ABSTRACT FROM AUTHOR]

High-speed biplanar videoradiography can derive the dynamic bony translations and rotations required for joint cartilage contact mechanics to provide insights into the mechanical processes and mechanisms of joint degeneration or pathology. A key challenge is the accurate registration of 3D bone models (from MRI or CT scans) with 2D X-ray image pairs. Marker-based or model-based 2D–3D registration can be performed. The former has higher registration accuracy owing to corresponding marker pairs. The latter avoids bead implantation and uses radiograph intensity or features. A rigorous new method based on projection strategy and least-squares estimation that can be used for both methods is proposed and validated by a 3D-printed bone with implanted beads. The results show that it can achieve greater marker-based registration accuracy than the state-of-the-art RSA method. Model-based registration achieved a 3D reconstruction accuracy of 0.79 mm. Systematic offsets between detected edges in the radiographs and their actual position were observed and modeled to improve the reconstruction accuracy to 0.56 mm (tibia) and 0.64 mm (femur). This method is demonstrated on in vivo data, achieving a registration precision of 0.68 mm (tibia) and 0.60 mm (femur). The proposed method allows the determination of accurate 3D kinematic parameters that can be used to calculate joint cartilage contact mechanics. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of unmanned aerial vehicle (UAV) technology, multi-view 3D reconstruction has once again become a hot spot in computer vision. Incremental Structure From Motion (SFM) is currently the most prevalent reconstruction pipeline, but it still faces challenges in reconstruction efficiency, accuracy, and feature matching. In this paper, we use deep learning algorithms for feature matching to obtain more accurate matching point pairs. Moreover, we adopted the improved Gauss–Newton (GN) method, which not only avoids numerical divergence but also accelerates the speed of bundle adjustment (BA). Then, the sparse point cloud reconstructed by SFM and the original image are used as the input of the depth estimation network to predict the depth map of each image. Finally, the depth map is fused to complete the reconstruction of dense point clouds. After experimental verification, the reconstructed dense point clouds have rich details and clear textures, and the integrity, overall accuracy, and reconstruction efficiency of the point clouds have been improved. [ABSTRACT FROM AUTHOR]

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features. [ABSTRACT FROM AUTHOR]

This work focuses on investigating the accuracy of 3D reconstructions from fixed stereo-photogrammetric monitoring systems through different camera calibration procedures. New reliable and effective calibration methodologies that require minimal effort and resources are presented. A full-format camera equipped with fixed 50 and 85 mm focal length optics is considered, but the methodologies are general and can be applied to other systems. Four different calibration strategies are considered: (i) full-field calibration (FF); (ii) multi-image on-the-job calibration (MI); (iii) point cloud-based calibration (PC); and (iv) self (on-the-job) calibration (SC). To evaluate the calibration strategies and assess their actual performance and practicality, two test sites are used. The full-field calibration, while very reliable, demands significant effort if it needs to be repeated. The multi-image strategy emerges as a favourable compromise, offering good results with minimal effort for its realisation. The point cloud-based method stands out as the optimal choice, balancing ease of implementation with quality results; however, it requires a reference 3D point cloud model. On-the-job calibration with monitoring images is the simplest but least reliable option, prone to uncertainty and potential inaccuracies, and should hence be avoided. Ultimately, prioritising result reliability over absolute accuracy is paramount in continuous monitoring systems. [ABSTRACT FROM AUTHOR]

With the increasing use of unmanned aerial vehicles (UAVs) in various fields, achieving the precise localization of UAV images is crucial for enhancing their utility. Photogrammetry-based techniques, particularly bundle adjustment, serve as foundational methods for accurately determining the spatial coordinates of UAV images. The effectiveness of bundle adjustment is significantly influenced by the selection of input data, particularly the composition of overlapping image sets. The selection process of overlapping images significantly impacts both the accuracy of spatial coordinate determination and the computational efficiency of UAV image localization. Therefore, a strategic approach to this selection is crucial for optimizing the performance of bundle adjustment in UAV image processing. In this context, we propose an efficient methodology for determining overlapping image sets. The proposed method selects overlapping images based on image similarity, leveraging the complementary strengths of the bag of visual words and number of matches techniques. Essentially, our method achieves both high accuracy and high speed by utilizing a Bag of Visual Words for candidate selection and the number of matches for additional similarity assessment for overlapping image-set determination. We compared the performance of our proposed methodology with the conventional number of matches and bag-of-visual word-based methods for overlapping image-set determination. In the comparative evaluation, the proposed method demonstrated an average precision of 96%, comparable to that of the number of matches-based approach, while surpassing the 62% precision achieved by both bag-of-visual-word methods. Moreover, the processing time decreased by approximately 0.11 times compared with the number of matches-based methods, demonstrating relatively high efficiency. Furthermore, in the bundle adjustment results using image sets, the proposed method, along with the number of matches-based methods, showed reprojection error values of less than 1, indicating relatively high accuracy and contributing to the improvement in accuracy in estimating image positions. [ABSTRACT FROM AUTHOR]

Multi-camera systems for structure from motion (SfM) are widely deployed in many mapping applications. Existing solutions assume known rig calibration, synchronized frames among cameras, as well as overlapping field of views (FoVs). In this paper, we derive novel geometric constraints assuming minimal knowns about the multi-camera systems, to benefit low-cost and non-expert use cases where uncalibrated multi-camera systems with non-typical geometry setups present, i.e., no rig calibration, no overlapping FoVs. Assuming that these cameras are co-located and share the same motion of the platform, the proposed constraints utilize the parallelism and length proportionality of motion vectors of these co-located cameras and formulate them as translation constraints into the bundle adjustment (BA). The proposed constraints (called motion constraints) impose a first-order penalty to co-located cameras whose motion speeds and directions between frames do not match. With soft constraints, it can handle loosely synchronized frames (with an error within one second). The proposed constraints are integrated into the BA framework and experimented with different camera setups, i.e., on a group of casually co-located GoPro cameras with no rig calibration, and some with no overlapping views. Our results show that the constraints are extremely effective in improving the reconstruction and pose accuracy for ground motion images: in our self-collected open trajectories without loop closure, the proposed constraints are effective in correcting topographical errors (i.e., trajectory drifts) of the resulting models, and the dense point clouds achieve up to 11.34 m (86.12 %) of mean absolute error (MAE) improvement as compared to reference LiDAR point clouds; our results on KITTI-odometry and KITTI-360 datasets also show an improvement of up to 28.82 m (81.05 %) in terms of the root mean square error (RMSE) of absolute pose error (APE). We expect that the proposed constraints are significant not only as additional geometric constraints for image-based mobile mapping, but also will benefit the broader use of photogrammetry, since it empowers the possibility to harness the traditionally so-called low-quality stereo/multi-camera data (e.g., by non-photogrammetry citizen scientists) into improved 3D products. [ABSTRACT FROM AUTHOR]

This paper primarily presents a parallel incremental Structure from Motion (ISfM) solution for large-scale images captured by unmanned aerial vehicles (UAVs). The core ideas are a local connection-constrained edge weighting strategy for match graph construction and an anchor-free parallel merging algorithm for the merged model generation. First, an effective algorithm is employed to retrieve spatially overlapped match pairs, utilizing the global descriptor for image representation and the graph indexing for nearest neighbor searching. Second, match pairs are used to create an undirected weighted match graph that is weighted by the local connection strength of the image. This match graph is then used to achieve parallel ISfM through graph clustering. Third, an anchor-free cluster merging algorithm, called AFP-Merging, is then designed by taking advantage of the independent connection between clusters, which increases the merging efficiency and stability. For robust estimation, AFP-Merging is implemented via a bidirectional mean square reprojection error. Finally, extensive evaluation and analysis have been carried out to verify its validation using large-scale UAV datasets captured from classical oblique photogrammetry and recent optimized views photogrammetry. Experiment results show that the proposed solution can generate more compact scene clusters and achieve a speedup ratio greater than 9.0 in cluster merging; compared with recent parallel ISfM, its orientation accuracy is higher in both relative bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA with GCPs. For the orientation of very large-scale UAV images, it has been successfully applied to a dataset containing ninety thousand images over an area of 50.0 km 2. The proposed method provides a more efficient and reliable parallel SfM solution. The executable tool is made publicly available 1 1 https://github.com/json87/ParallelSfM.. [ABSTRACT FROM AUTHOR]

This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs). [ABSTRACT FROM AUTHOR]

We design a scheme for laser-inertial odometry and mapping with bundle adjustment (BA-LIOM), which can greatly mitigate the problem of undesired ground warping due to sparsity of laser scans and significantly reduce odometry drift. Specifically, an Inertial measurement unit (IMU)-assisted adaptive voxel map initialization algorithm is proposed and elaborately integrated with the existing framework LIO-SAM, allowing for accurate registration in the beginning of the localization and mapping process. In addition, to accommodate to fast-moving and structure-less scenarios, we design a tightly coupled odometry, which jointly optimizes both the IMU preintegration constraints and scan matching with adaptive voxel maps. The voxels (edge and plane, respectively) are updated with BA optimization. And then the accurate mapping result is obtained by performing local BA. The proposed BA-LIOM is thoroughly assessed using datasets collected from multiple platforms over a variety of environments. Experimental results show the superiority of BA-LIOM over the state-of-the-art methods in robustness and precision, especially for large-scale scenarios. BA-LIOM improves the accuracy of localization by $61\%$ and $73\%$ on the buildings and lawn datasets, respectively, and has a $29\%$ accuracy improvement over LIO-SAM on the KITTI datasets. A supplementary video can be accessed at https://youtu.be/5l4ZFhTc2sw. [ABSTRACT FROM AUTHOR]

Image-based methods have been widely used in augmented reality (AR) assistant assembly systems. However, due to the lack of sufficient texture information on the surface of assembly part, traditional image feature matching methods still face challenges. This paper proposes a coarse-to-fine AR registration method for textureless assembly part. In the first stage, a new feature matching method which is called line neighborhood edge descriptor (LNED) is presented to find the coarse camera pose from textureless image. The LNED take the contour line of assembly part as the description object, and use local geometric edge of assembly part to describe the contour line. During the image matching, the binary encoding is used to reduce the computational consumption for LNED. In the second stage, spatial points in the CAD model of assembly part are reverse projected to the textureless image based on the coarse camera pose. And the bundle adjustment method based on the edge distance of the textureless image is adopted to iteratively calculate the precise camera pose. In the experimental evaluation, the proposed registration method shows high accuracy and fast speed in comparison with conventional registration methods, which demonstrates that our method can effectively solve the problem of AR registration for textureless assembly part. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method.

A semantic SLAM algorithm based on dynamic object tracking is proposed to address the issue of decreased localization accuracy in traditional visual SLAM methods due to feature matching errors in dynamic scenes. The algorithm, built upon the classic visual SLAM framework, extracted dynamic objects for inter-frame tracking and utilized their pose information to assist the camera’s own localization. Firstly, YOLACT, RAFT, and SC-Depth networks were employed in the data preprocessing stage to extract semantic masks, optical flow vectors, and pixel depths from the images. Subsequently, the visual front-end module utilized the extracted information to compute probability maps,employing semantic segmentation masks, motion consistency checks, and occlusion point verification algorithms.These probability maps aided in effectively distinguishing between dynamic and static features in the scene. Then, the bundle adjustment module in the back-end integrated multiple feature constraints derived from object motion to enhance the algorithm’s pose estimation performance in dynamic scenes. Finally, comprehensive comparisons and validations were conducted on the dynamic scenes of the KITTI and OMD datasets. The experimental results demonstrate that the proposed algorithm accurately tracks dynamic objects and exhibits robust and accurate localization performance in both indoor and outdoor dynamic scenes. [ABSTRACT FROM AUTHOR]

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With the increasing use of unmanned aerial vehicles (UAVs) in various fields, achieving the precise localization of UAV images is crucial for enhancing their utility. Photogrammetry-based techniques, particularly bundle adjustment, serve as foundational methods for accurately determining the spatial coordinates of UAV images. The effectiveness of bundle adjustment is significantly influenced by the selection of input data, particularly the composition of overlapping image sets. The selection process of overlapping images significantly impacts both the accuracy of spatial coordinate determination and the computational efficiency of UAV image localization. Therefore, a strategic approach to this selection is crucial for optimizing the performance of bundle adjustment in UAV image processing. In this context, we propose an efficient methodology for determining overlapping image sets. The proposed method selects overlapping images based on image similarity, leveraging the complementary strengths of the bag of visual words and number of matches techniques. Essentially, our method achieves both high accuracy and high speed by utilizing a Bag of Visual Words for candidate selection and the number of matches for additional similarity assessment for overlapping image-set determination. We compared the performance of our proposed methodology with the conventional number of matches and bag-of-visual word-based methods for overlapping image-set determination. In the comparative evaluation, the proposed method demonstrated an average precision of 96%, comparable to that of the number of matches-based approach, while surpassing the 62% precision achieved by both bag-of-visual-word methods. Moreover, the processing time decreased by approximately 0.11 times compared with the number of matches-based methods, demonstrating relatively high efficiency. Furthermore, in the bundle adjustment results using image sets, the proposed method, along with the number of matches-based methods, showed reprojection error values of less than 1, indicating relatively high accuracy and contributing to the improvement in accuracy in estimating image positions.

This paper examines the effects of implementing relative orientation constraints on bundle adjustment, as well as provides a full derivation of the Jacobian matrix for such an adjustment, that can be used to facilitate other implementations of bundle adjustment with constrained cameras. We present empirical evidence demonstrating improved accuracy and reduced computational load when these constraints are imposed.

Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that divides the image sensor and distortion modeling into two concentric zones for the application of an extended radial lens distortion model. The mathematical model is explained in detail and it was validated on image data from a DJI Mavic Pro UAV camera. First, the special distortion pattern of the camera was examined by decomposing and analyzing the residuals. Then, a novel bi-radial model was introduced to describe the pattern. Eventually, the new model was integrated in a bundle adjustment software package. Practical tests revealed that the residuals of the bundle adjustment could be reduced by 63% with respect to the standard Brown model. On the basis of external reference measurements, an overall reduction in the residual errors of 40% was shown. [ABSTRACT FROM AUTHOR]

Visual odometry and visual simultaneous localization and mapping aid in tracking the position of a camera and mapping the surroundings using images. It is an important part of robotic perception. Tracking and mapping using a monocular camera is cost-effective, requires less calibration effort, and is easy to deploy across a wide range of applications. This paper provides an extensive review of the developments for the first two decades of the twenty-first century. Astounding results from early methods based on filtering have intrigued the community to extend these algorithms using other forms of techniques like bundle adjustment and deep learning. This article starts by introducing the basic sensor systems and analyzing the evolution of monocular tracking and mapping algorithms through bibliometric data. Then, it covers the overview of filtering and bundle adjustment methods, followed by recent advancements in methods using deep learning with the mathematical constraints applied on the networks. Finally, the popular benchmarks available for developing and evaluating these algorithms are presented along with a comparative study on a different class of algorithms. It is anticipated that this article will serve as the latest introductory tool and further ignite the interest of the community to solve current and future impediments. [ABSTRACT FROM AUTHOR]

Much work has been done to improve visual SLAM systems by integrating point, line, and plane features into the bundle adjustment model, however little attention has been paid to explore the associations between these spatial features that could be used to achieve better performance. This study proposes a multimodal feature association-based stereo SLAM method. Firstly, a method to extract point and line features from stereo images and estimate plane features through the line features is proposed. Then, the corresponding mathematic models for representing the association relations between different features are given. After that, the association relations are integrated into the back-end optimization model to improve system robustness and accuracy by adjusting reprojection errors with confidence weights. Finally, comparison tests are performed to evaluate the performance of the proposed method with the mainstream stereo SLAM systems using the EuRoC and KITTI datasets. The test results show that our approach not only generates semantic maps with higher fidelity but also provides a better positioning capability, with the positioning accuracy of the algorithm on the EuRoC and KITTI datasets improved by an average of 14.78% and 20.09%, respectively. [ABSTRACT FROM AUTHOR]

In this study, the problem of bundle adjustment was revisited, and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment. The innovation points are reflected in the following aspects: ① The proposed algorithm is not dependent on the Schur complement, and the calculation process is simple and clear; ② The complexities of time and space tend to O(n) in the context of world point number is far greater than that of images and cameras, so the calculation magnitude and memory consumption can be reduced significantly; ③ The proposed algorithm can carry out self-calibration bundle adjustment in single-camera, multi-camera, and variable-camera modes; ④ Some measures are employed to improve the optimization effects. Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness, and it has a strong adaptability as well, because the optimized results are accurate and robust even if the initial values have large deviations from the truth. This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry, computer vision and robotics.

We developed the first-ever bathymetric module for the NASA Ames Stereo Pipeline (ASP) open-source topographic software called Satellite Triangulated Sea Depth, or SaTSeaD, to derive nearshore bathymetry from stereo imagery. Correct bathymetry measurements depend on water surface elevation, and whereas previous methods considered the water surface horizontal, our bathymetric module accounts for the curvature of the Earth in the imagery. The process is semiautomatic, reliable, and repeatable, independent of any external bathymetry data eliminating user bias in selecting bathymetry calibration points, and it can generate a fully integrated and seamless topo-bathymetry digital elevation model (TBDEM) in the same coordinate system, comparable with the band-ratio method irrespective of the regression method used for the band-ratio algorithm. The ASP output can be improved by applying a camera bundle adjustment to minimize reprojection errors and by alignment to a more accurate topographic (above water) surface without any bathymetric input since the derived TBDEM is a rigid surface. These procedures can decrease bathymetry root mean square errors from 30 to 80 percent, depending on environmental conditions, the quality of satellite imagery, and the spectral band used (e.g., blue, green, or panchromatic). [ABSTRACT FROM AUTHOR]

In this research, we proposed a stereo visual simultaneous localisation and mapping (SLAM) system that efficiently works in agricultural scenarios without compromising the performance and accuracy in contrast to the other state-of-the-art methods. The proposed system is equipped with an image enhancement technique for the ORB point and LSD line features recovery, which enables it to work in broader scenarios and gives extensive spatial information from the low-light and hazy agricultural environment. Firstly, the method has been tested on the standard dataset, i.e., KITTI and EuRoC, to validate the localisation accuracy by comparing it with the other state-of-the-art methods, namely VINS-SLAM, PL-SLAM, and ORB-SLAM2. The experimental results evidence that the proposed method obtains superior localisation and mapping accuracy than the other visual SLAM methods. Secondly, the proposed method is tested on the ROSARIO dataset, our low-light agricultural dataset, and O-HAZE dataset to validate the performance in agricultural environments. In such cases, while other methods fail to operate in such complex agricultural environments, our method successfully operates with high localisation and mapping accuracy. [ABSTRACT FROM AUTHOR]

Fully perceiving the surrounding world is a vital capability for autonomous robots. To achieve this goal, a multi-camera system is usually equipped on the data collecting platform and the structure from motion (SfM) technology is used for scene reconstruction. However, although incremental SfM achieves high-precision modeling, it is inefficient and prone to scene drift in large-scale reconstruction tasks. In this paper, we propose a tailored incremental SfM framework for multi-camera systems, where the internal relative poses between cameras can not only be calibrated automatically but also serve as an additional constraint to improve the system robustness. Previous multi-camera based modeling work has mainly focused on stereo setups or multi-camera systems with known calibration information, but we allow arbitrary configurations and only require images as input. First, one camera is selected as the reference camera, and the other cameras in the multi-camera system are denoted as non-reference cameras. Based on the pose relationship between the reference and non-reference camera, the non-reference camera pose can be derived from the reference camera pose and internal relative poses. Then, a two-stage multi-camera based camera registration module is proposed, where the internal relative poses are computed first by local motion averaging, and then the rigid units are registered incrementally. Finally, a multi-camera based bundle adjustment is put forth to iteratively refine the reference camera and the internal relative poses. Experiments demonstrate that our system achieves higher accuracy and robustness on benchmark data compared to the state-of-the-art SfM and SLAM (simultaneous localization and mapping) methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are used to acquire measurement data for an increasing number of applications. Photogrammetric studies based on UAV data, thanks to the significant development of computer vision techniques, photogrammetry, and equipment miniaturization, allow sufficient accuracy for many engineering and non-engineering applications to be achieved. In addition to accuracy, development time and cost of data acquisition and processing are also important issues. The aim of this paper is to present potential limitations in the use of UAVs to acquire measurement data and to present measurement and processing techniques affecting the optimisation of work both in terms of accuracy and economy. Issues related to the type of drones used (multi-rotor, fixed-wing), type of shutter in the camera (rolling shutter, global shutter), camera calibration method (pre-calibration, self-calibration), georeferencing method (direct, indirect), technique of measuring the external images orientation parameters (RTK, PPK, PPP), flight design methods and the type of software used were analysed. [ABSTRACT FROM AUTHOR]

Objectives: The control network in accelerator alignment often forms a straight line or ring. The errors will accumulate as the number of stations increases when performing lap measurements with the laser tracker. To improve the measurement accuracy of the laser tracker control network, we analyze the source of errors and compare four different measurement methods. Methods: First, the formulas of error propagation in the bundle adjustment method are derived and the error source of the unknown points is analyzed. Second, the following four schemes are obtained by adopting the bundle adjustment method of laser trackers. Finally, the performances of the above schemes are analyzed in the experiment of bundle adjustment. Results: The results show that the absolute position accuracy for the scheme with fixed position and orientation is the highest among the four schemes. The average root mean square (RMS) of the position is 0.147 mm in the experiment of the closed measurement, which is less than that of the unclosed measurement with the RMS of 0.163 mm. In the measurement range of 15 m × 10 m × 3 m, the orientation of the non-fixed scheme flat-rate solution and the average RMS of the plane position are 3.58 s and 0.144 mm, respectively. The station closure can enhance the constraint. Besides, the fixed station center position can effectively inhibit the error accumulation of multi-station lap measurement, improving the accuracy of the network adjustment. Moreover, the result of fixed station center position is better than that of the fixed station center orientation, which indicates that the station positions are vital parameters that affect the two-dimensional bundle adjustment of the laser tracker. Conclusion: This paper can provide a reference for the design of the high-precision laser tracker bundle adjustment method. [ABSTRACT FROM AUTHOR]

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation.

Three-dimensional (3D) real scale models delivered from digital photogrammetric techniques have rapidly increased to meet the requirements of many applications in different fields of daily life. This paper deals with the establishment of a 3D real scale model from a block of images (18 images) that were captured by using Canon EOS 500D digital camera to cover a test field area consisting of 90 artificial target points, 25 of them are ground control points (GCPs) while the remains are checkpoints (CPs). The analytical photogrammetric processes including the calculation of interior orientation parameters (IOPs) of the camera during the camera calibration process, exterior orientation parameters (EOPs) of the camera in each capturing, and the object space (ground) coordinates of the model are calculated simultaneously based on collinearity equation using bundle block adjustment method (BBA). Assessment and validation of the accuracy of the results is an important task in this study that was implemented to determine and analyze the errors of 3D coordinates through linear regression analysis (LRA). Root mean square error (RMSE) is the statistical parameter that was used in the statistical analysis of results. The standard error is another statistical parameter which also used to evaluate the accuracy of locations and rotation angles (EOPs) of cameras. The total RMSE (RMSE)xyz of GCPs is ± 2.530 mm while the total RMSE (RMSExyz) of CPs is ± 2.740 mm. The overall accuracy of the work is 5.000 mm.

We propose RLP‐VIO—a robust and lightweight monocular visual‐inertial odometry system using multiplane priors. With planes extracted from the point cloud, visual‐inertial‐plane PnP uses the plane information for fast localization. Depth estimation is susceptible to degenerated motion, so the planes are expanded in a reprojection consensus‐based way robust to depth errors. For sensor fusion, our sliding‐window optimization uses a novel structureless plane‐distance error cost, which prevents the fill‐in effect that poisons the BA problem's sparsity and permits the use of a smaller sliding window while maintaining good accuracy. The total computational cost is further reduced with our modified marginalization strategy. To further improve the tracking robustness, the landmark depths are constrained using the planes during degenerated motion. The whole system is parallelized with a three‐stage pipeline. Under controlled environments, this parallelization runs deterministically and produces consistent results. The resulting VIO system is tested on widely used datasets and compared with several state‐of‐the‐art systems. Our system achieves competitive accuracy and works robustly even on long and challenging sequences. To demonstrate the effectiveness of the proposed system, we also show the AR application running on mobile devices in real‐time. [ABSTRACT FROM AUTHOR]

The key constituent of simultaneous localization and mapping (SLAM) is the joint optimization of sensor trajectory estimation and 3D map construction. The multivariable optimization process in SLAM is mainly carried out through bundle adjustment (BA). However, the method of handling outliers in actual data directly affects the accuracy of BA optimization and further affects the tracking performance of the system. In addition, in monocular initialization process, when the feature points are coplanar or have low parallax, their fundamental matrix will degrade and greatly affects the initial pose estimation results. To further surmount the above challenges, this paper presents a real time monocular visual SLAM optimization based on BA with adaptive robust kernel (ARK-SLAM): 1) a model selection mechanism with geometric robust information content is designed to improve the robustness of monocular initialization; 2) an adaptive robust kernel based BA is proposed to reduce the interference of outliers and improve the accuracy of optimal pose estimation; 3) a loop closure candidate verification scheme based on adaptive robust kernel is introduced to jointly minimize the geometric error term and the relative pose constraints. The position tracking performance of developed ARK-SLAM method is verified using TUM RGB-D benchmark dataset and KITTI dataset, and experimental results illustrate the favorable performances of ARK-SLAM by comparing with ORB-SLAM, ORB-SLAM3, LSD-SLAM and PTAM. [ABSTRACT FROM AUTHOR]

• Improved accuracy of circular marker-aided multi-view point cloud registration. • An AWBA method is proposed to optimise multi-view point cloud registration. • Adaptive weighted markers 3D coordinates are used to improve registration quality. • Combining local and global optimization to enhance registration accuracy. • Accurate registration has implications for promoting industrial development. To address the issue of cumulative error leading to poor registration results in multi-view laser point cloud registration aided by circular markers, caused by the reconstruction error of the three-dimensional (3D) coordinates of marker centres and local view transformation matrix estimation error, an Adaptive-Weighted Bundle Adjustment (AWBA) method is proposed. Firstly, coarse registration is achieved based on Euclidean distance matching and angle constraints. Then, an adaptive weighting strategy is introduced to incorporate the accuracy information into the computation of the transformation matrix to improve its estimation accuracy and inhibit the single-view registration error by considering the accuracy difference of the circle centre 3D coordinates. Next, global marker coordinates are optimised by first removing outliers from the sets of homologous points using statistical methods followed by iteratively solving the global marker coordinates using remaining weighted homologous points to improve the accuracy of the global marker. AWBA adopts a synchronous optimisation strategy to calculate the current view transformation matrix based on the latest optimised global markers when the data of a new view is acquired, and it continuously optimises the coordinates of the global marker throughout the reconstruction process to suppress the backward cumulative error. Experimental results demonstrate that AWBA enjoys state-of-the-art performance compared with other methods, with Absolute Error (AE) <0.094 mm for the standard ball radius, model Mean Absolute Distance (MAD) <0.093 mm, and Successful Registration Rate (SRR) greater than 93.010. AWBA can enhance the registration effect of multi-view laser point clouds with a wide range of applications in industrial inspection, robotic navigation and cultural heritage preservation. [ABSTRACT FROM AUTHOR]

The 3D reconstruction process is very important in a variety of computer vision applications. Bundle adjustment has a significant impact on 3D reconstruction processes, namely in Simultaneously Localization and Mapping (SLAM) and Structure from Motion (SfM). Bundle adjustment, which optimizes camera parameters and 3D points as a very important final stage, suffers from memory and efficiency requirements in very large-scale reconstruction. Multi-objective optimization (MOO) is used in solving a variety of realistic engineering problems. Multi-Objective Particle Swarm Optimization (MOPSO) is regarded as one of the state of the art for meta-heuristic MOO. MOPSO has utilized the concept of crowding distance as a measure to differentiate between solutions in the search space and provide a high level of exploration. However, this method ignores the direction of the exploration which is not sufficient to effectively explore the search space. In addition, MOPSO starts the search from a fully randomly initialized swarm without taking any prior knowledge about the initial guess into account, which is considered impractical in applications where we can estimate initial values for solutions like bundle adjustment. In this paper, we introduced a novel hybrid MOPSO-based bundle adjustment algorithm that takes advantage of initial guess, angle quantization technique, and traditional optimization algorithms like RADAM to improve the mobility of MOPSO solutions; the results showed that our algorithm can help improve the accuracy and efficiency of bundle adjustment (BA).