Leveraging line features to improve the accuracy of the SLAM system has been studied in many works. However, making full use of the characteristics of different line features (parallel, non-parallel) to improve the SLAM system is rarely mentioned. In this paper, we designed a VIO system based on points and straight lines, which divides straight lines into structural (that is, straight lines parallel to each other) and non-structural. To optimize the line features effectively, we used two-parameter representation methods for both structural and non-structural straight lines. Furthermore, we designed a stable line matching method based on frame-to-frame (2D-2D) and frame-to-map (2D-3D) strategies which can significantly improve the trajectory accuracy of the system. We conducted ablation experiments on synthetic data and public datasets, and also compared our method with state-of-the-art algorithms. The experiments verified the combination of different line features can improve the accuracy of the VIO system, and also demonstrated the effectiveness of our system.

Accurate, continuous, and reliable positioning is critical to achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of stand-alone sensors and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view image segmentation algorithm based on a fully convolutional network (FCN) is proposed for NLOS detection in global navigation satellite systems (GNSSs). Building upon this, a novel NLOS detection and mitigation algorithm (named S−NDM) uses a tightly coupled GNSS, inertial measurement units (IMUs), and a visual feature system called Sky−GVIO with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system combines single-point positioning (SPP) with real-time kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of the S−NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP−related and RTK−related models. The results exhibit that the Sky−GVIO system achieves meter-level accuracy under the SPP mode and sub-decimeter precision with RTK positioning, surpassing the performance of GNSS/INS/Vision frameworks devoid of S−NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration.

Mobile platform visual image sequence inevitably has large areas with various types of weak textures, which affect the acquisition of accurate pose in the subsequent platform moving process. The visual–inertial odometry (VIO) with point features and line features as visual information shows a good performance in weak texture environments, which can solve these problems to a certain extent. However, the extraction and matching of line features are time consuming, and reasonable weights between the point and line features are hard to estimate, which makes it difficult to accurately track the pose of the platform in real time. In order to overcome the deficiency, an improved effective point–line visual–inertial odometry system is proposed in this paper, which makes use of geometric information of line features and combines with pixel correlation coefficient to match the line features. Furthermore, this system uses the Helmert variance component estimation method to adjust weights between point features and line features. Comprehensive experimental results on the two datasets of EuRoc MAV and PennCOSYVIO demonstrate that the point–line visual–inertial odometry system developed in this paper achieved significant improvements in both localization accuracy and efficiency compared with several state-of-the-art VIO systems.

Unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images. Thus, accurate, stable, and well-distributed matches to follow the accurate pose estimation of the platform are difficult to obtain. To solve such problems, an effective image matching method for mobile platform visual imagery is presented in this study. The proposed method includes three steps, namely, standard initial matching, transformation matrices evaluation and matching propagation. Firstly, an oriented FAST and rotated BRIEF (ORB) method was used to obtain the number of matches and the initial projective transformation relationship between an image pair. Secondly, an evaluation function was set to choose the suitable rotation matrix for the image scene. Finally, geometric correspondence matching was utilized to propagate matches and produce additional reliable matching results. The geometric correspondence matching used the geometric relationship between the image pair and found more suitable matches than the standard ORB matching. Comprehensive experiments on TUM and ICL-NUIM dataset images showed that the proposed algorithm performs better in terms of correct matches, satisfactory matching rate, and higher matching accuracy than the standard ORB and ORB-slam2 initial match methods.

The monocular visual-inertial odometry (VIO) based on the direct method can leverage all available pixels in the image to simultaneously estimate the camera motion and reconstruct the denser map of the scene in real time. However, the direct method is sensitive to photometric changes, which can be compensated by introducing geometric information in the environment. In this paper, we propose a monocular direct sparse visual-inertial odometry, which exploits the planar regularities (PVI-DSO). Our system detects the planar regularities from the 3D mesh built on the estimated map points. To improve the pose estimation accuracy with the geometric information, a tightly coupled coplanar constraint expression is used to express photometric error in the direct method. Additionally, to improve the optimization efficiency, we elaborately derive the analytical Jacobian of the linearization form for the coplanar constraint. Finally, the inertial measurement error, coplanar point photometric error, non-coplanar photometric error, and prior error are added into the optimizer, which simultaneously improves the pose estimation accuracy and mesh itself. We verified the performance of the whole system on simulation and real-world datasets. Extensive experiments have demonstrated that our system outperforms the state-of-the-art counterparts.

Monocular vision sensors are often affected by the rapid direction change of load platform and violent illumination change when the mobile device moves autonomously with high maneuverability. The images collected by the visual sensor will also have a lot of dynamic blur, which together with the weak texture environment reduces the continuity and accuracy of the visual autonomous navigation system. In order to enhance the stability of the system, in the article, we propose a vision-led multi-source data fusion navigation algorithm. The system combines visual information for trajectory estimation, adds the IMU measurement information to the sliding window for optimization, and finally use the GNSS data as a re-constraint condition through factor graph optimization to further optimize the trajectory accuracy. Experiments on the public datasets containing a variety of different scene categories show that the trajectory tracking results generated by our algorithm are more complete and stable, and can better meet the system’s autonomous navigation requirements.