Tightly-coupled stereo visual-inertial navigation using point and line features.

At present, most of the visual Simultaneous Localization and Mapping (SLAM) systems rely on the surrounding point features to achieve acceptable localization and mapping. However, the number of point features is insufficient in the low-texture environments, so the performance of these SLAM systems will be significantly reduced. In this research, a PLISLAM system that integrates point features, line features and Inertial Measurement Unit (IMU) is proposed to implement high-precision positioning and mapping for dynamic vehicles. Specifically, a state-of-the-art SLAM scheme ORBSLAM3 is built at first. Then, its theoretical formulation is derived step by step to handle the environmental line features and the Bundle Adjustment (BA) is integrated to optimize the data. Finally, the system performance is verified through the EuRoC dataset, the results demonstrate its accuracy could be improved by adding the line features especially in scenes with rich line features.

Most of the existing visual SLAM schemes rely solely on point or line features to estimate camera trajectory. In some scenes such as texture missing or motion blurring, it’s difficult to find a sufficient number of reliable features, resulting in low positioning accuracy. To extract more features, an RPL-SLAM solution is proposed to extract point features and line features respectively. Ulteriorly, the depth information of RGBD image is used to restore the 3D information of point and line features, improving the accuracy of camera track positioning. RPL-SLAM scheme mainly includes three modules: tracking, local mapping and loop detection. Tracking module extends the application of line feature on the basis of point feature extraction and matching. A SLD line segment extraction algorithm which can eliminate the micro segments and a DBM segment matching algorithm based on word bag are proposed respectively. These two algorithms improve the matching efficiency while ensuring the matching accuracy, and effectively track and locate the camera of each frame. In the local mapping and loop detection module, the Plucker coordinates are applied to express the spatial line and define the re-projection error of the straight line, so that the back-end optimization error model of point-line fusion is unified to solve the instability problem in optimization. RPL-SLAM is tested on TUM RGBD and ICL-NUIM data set respectively, and compared with ORB-SLAM2. The result shows that RPL-SLAM can effectively improve the accuracy of pose estimation and map reconstruction while maintaining real-time performance by fusing point-line features with depth images.

For the visual-inertial system, the key frame loss always emerges in the rotation with the weak texture and strong illumination, which seriously affects the camera positioning accuracy. To solve this problem, a tight-coupling-based visual fusion scheme leveraging line features is proposed in our work, called VinsFusion-Line. It first deeply integrates line features, inertial measurement unit(IMU) pre-integration, and stereo visual observations through a tightly-coupled framework. And the infinitely extended line features provide more geometric and structural information, compared with the base point and line feature tracing methods. Hence, the positioning accuracy is improved greatly. In order to further make the calculation of 3D spatial line features faster and more efficient, Plücker coordinates and straight lines are used to represent the features. In addition, the camera state is optimized by a multi-source cost function that embeds both IMU pre-integration residuals and line feature reprojection errors. This design enables tight-coupled fusion of IMU and binocular visual information, rather than relying solely on point features as in existing methods. Finally, experiments on the public data show that under the strong light and weak texture, the binocular VinsFusion-Line system with infinite extension line in our work has better positioning accuracy than the single-purpose Vins-Fusion system.

Most mainstream visual-inertial SLAM systems rely on the point feature method of front-end images for motion estimation and localization. However, the SLAM system based on the point feature method, in the case of weakly textured environments, may not be able to estimate the motion due to the lack of point features. For this reason, the introduction of line features has received extensive attention. Line features in the environment can improve the localization accuracy of SLAM, but the extraction and matching of line features are relatively time-consuming and cannot satisfy real-time requirements. Based on the EDLines algorithm, we propose a fast visual-inertial odometry that fuses point and line features. The improved EDLines algorithm is used to process line features. By adjusting the internal parameters of the algorithm, the extraction time of line features is decreased while the extraction effect is the same. And the line features screening strategy is proposed to filter out invalid short line segments and merge adjacent line segments. We use the more effective line features after screening for graph matching and back-end optimization, which reduces the calculation amount of line features matching and ensures the accuracy. The proposed algorithm has been tested on the public datasets, and the comparison results with PL-VIO and VINS-Mono show that the algorithm in this paper improved real-time performance and localization accuracy.

Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting

This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.

PurposeThis paper aims to enable the unmanned aerial vehicles to inspect the surface condition of wind turbine in close range when the global positioning system signal is not reliable, and further improve its intelligence. So a visual-inertial odometry with point and line features is developed.Design/methodology/approachVisual front-end combining point and line features, as well as its purification strategies, are first presented to improve the robustness of feature tracking in low-textured scene and rapidity of segment detector. Additionally, the inertial measurement is integrated between keyframes as constrain to reduce tracking error existed in visual-only system. Second, the graph-based visual-inertial back-end is constructed. To parameterize line features effectively, the infinite line representation not sensitive to outdoor light is employed, in which Plücker and Cayley are selected for line re-projection and nonlinear optimization. Furthermore, Jacobians of the line re-projection errors are analytically derived for better accuracy.FindingsExperiments are performed in various scenes of the wind farm. The results demonstrate that the tight-coupled visual-inertial odometry with point and line features is more precise on all the samples than conventional algorithms in complex wind farm environments. Additionally, the constructed line feature map can be used in the following research for autonomous navigation.Originality/valueThe proposed visual-inertial odometry works robustly in strong electromagnetic interference, low-textured and illumination-change wind farm.

The feature extraction of a point cloud fragment model is the basis of fragment splicing, which provides the technical support for research on the segmentation, splicing, and restoration of fragment surfaces. High-quality feature extraction, however, is a complicated process due to the diversity of the surface information of a fragment model. For this subject, a high-efficient point cloud feature extraction method was proposed to address a new method for extracting feature lines. First, the projection distance feature of the point cloud model was calculated to identify the potential feature points. Furthermore, the local information of the possible feature points was used to construct the adaptive neighborhoods for identifying the feature points based on neighborhoods of the model. The clustering fusion of the feature points was proposed according to the discrimination threshold values of the feature points. Finally, the Laplace operator was utilized to refine and connect the feature points to form smooth feature lines. The experimental results showed that the proposed method was automatic, highly efficient, and with good adaptability that could effectively extract the detailed features and construct the complete feature lines. Moreover, results showed that the provided framework could extract the features of simple structure models and be feasible to a certain extent for fragment models with abundant features.

In indoor dynamic scenes, traditional visual simultaneous localization and mapping (SLAM) algorithms based on RGB-D cameras incorrectly use dynamic features to estimate the poses of the cameras, and do not fully utilize the geometric information in the scenes, resulting in the low positioning accuracy and robustness of SLAM systems. To solve this problem, this paper proposes an RGB-D SLAM algorithm based on multiple geometric features and semantic segmentation. The core of our SLAM system is the proposed robust exclusion method of dynamic point and line features. The method consists of the following three steps: (a) identify potential dynamic point features using motion consistency checks; (b) obtain potential motion regions via semantic segmentation, and then determine dynamic regions by combining these with dynamic point features; (c) remove point and line features in dynamic regions. The exclusion method of dynamic point and line features can be easily integrated into RGB-D SLAM systems for improving the accuracy and robustness of SLAM systems in dynamic scenes. Experimental results on the Technische Universität München (TUM) dataset demonstrate that the proposed algorithm has better positioning accuracy and stability than the original dynamic semantic (DS-SLAM) algorithm in dynamic environments. The effectiveness of the proposed algorithm is verified by comparison with other classical visual SLAM algorithms. Better mapping performance is achieved by this proposed algorithm in actual indoor scenes.

Contact-based aerial interaction control in unknown environments is a challenging issue. To solve this problem, this paper presents an image-based impedance control scheme for an aerial manipulator. Unlike points and image moments, the line features considered in this paper cannot uniquely determine the pose of the onboard camera. Therefore, a nonlinear observer is proposed to online provide supplementary 3D information of the system. After that, a hierarchical tracking controller, which is equipped with an image-space impedance filter, is designed to implement a trajectory tracking task with tunable compliance, while at the same time achieving a subtask that aims to determine the final pose of the aerial manipulator. By planning task-space trajectories, desired interaction behavior can be specified without relying on the global position of the system. Furthermore, the effectiveness criterion of the observer is derived, and the stability of the system is proven. Several experiments, including a push-and-slide task, robustness analysis, and a comparison with the state of the art have been conducted to demonstrate the feasibility of the method. Note to Practitioners—This article presents a method for controlling an aerial manipulator to interact with unknown environments without measuring the global position of both the environment and the aerial manipulator. Furthermore, unlike the existing methods, the proposed visual servoing method is based on line features, which are generally more robust than point features. Moreover, it would be intuitive for users to specify the approaching path and the interaction behavior of the aerial manipulator by just planning the image feature trajectories and the desired distance between the aerial manipulator and the environment. This distance is online estimated using the visual feedback of a monocular camera, instead of relying on any costly 3D-vision sensors or lidars, making it an economical solution for aerial manipulation in field environments.

Archeological line drawings are essential in archeological research, providing visual representations of artifact morphology and precise geometric measurements. Traditionally, these drawings were meticulously created by hand, a process that is time-consuming and labor-intensive. To overcome these limitations, this paper presents an algorithm for extracting feature lines from sculpture point cloud, enabling the automated generation of archeological line drawings. The method introduces a weighted centroid-based geometric metric to identify surface feature points and classify them based on their concavity or convexity. An iterative refinement process is then applied to ensure that feature points align correctly along the feature lines, and boundary points are extracted using an angular criterion. Finally, an enhanced curve-growing algorithm connects the feature and boundary points, producing a 3D line drawing. Experiments conducted on various stone sculptures demonstrate the practicality and accuracy of the proposed approach, with results showing significant improvements over existing methods.

A method of feature line extraction from triangular mesh model is presented. Firstly, initial feature point is manually specified based on feature points extraction from triangular mesh model. Then begin with initial feature point, Principal Component Analysis (PCA) method is applied to analyze primary direction of special feature region of feature points. Search the feature point which is furthest from the center of mass in the primary direction and it is chosen as subsequent feature point. Iteratively, record the feature point sequence in order. Finally, use three order non-uniform B-spline curve to fit and construct feature line. The experimental results demonstrate that the proposed method in this paper is efficient.

Method for fundamental matrix estimation combined with feature lines