The point feature, whose dynamic robustness has been widely studied, dominates in the field of visual simultaneous localization and mapping (SLAM) or visual-inertial odometry (VIO). When discussing a dynamic scene, line features are not given enough attention. This paper proposes a pre-processing step for VIO to reduce the influence of lines upon dynamic objects on system robustness and merges it into a state-of-the-art optimization-based VIO pipeline. First, it is determined whether the line feature is a potential dynamic line based upon the result of the semantic segmentation, optical flow and re-projection error. Then, instead of filtering them out, the information matrixes of these line features in the optimization function is adjusted by a weight-based method according to their tracked size. A simulated challenged visual-inertial dataset is used to evaluate the proposed algorithm against other state-of-the-art methods. The results shows that proposed method can increase robustness to dynamic scenes and make the line-based map neater and more intuitive through avoiding the drawing of dynamic line features during the mapping procedure.

The existing Visual-inertial-LiDAR localization methods lack consideration of sensors degradation in challenging scene, such as underground space, where light condition is poor, text feature is scarce and geometric structure is degraded. To enhance the robustness of the multi-sensor fusion, this paper proposes a confidence factor based robust localization algorithm. It mainly consists of two parts. In front-end, a lightweight DCE-Net is used to improve the image quality under low illumination, and then an improved feature extraction method based on Line Segment Detector and Manhattan World (LSD-MW) is proposed to extract robust point-line features and construct Manhattan Frame (MF) structure constraint for subsequent optimization, making visual feature more reliable in underground space. In back-end, a novel confidence factor graph optimization strategy is proposed to enhance the robustness in the case of sensor degradation. Where adaptive confidence factors are designed to assess the reliability of LiDAR and camera features based on their feature matching degree. These confidence factors are leveraged to weight the sensors residual factors to construct an iterative function based on Graduated Nonconvexity (GNC), mitigating the influence of outliers on state estimation in degradation scene of underground space. Experiments conducted on public and real-world dataset collected by the UAV platform we built verify the proposed method has promising performance. Ablation studies also show the robustness of our method.

To solve the issue that LiDAR simultaneous localization and mapping (SLAM) suffers from geometric degenerate environment, several works have sought supplementary measurement information from camera instruments, while ignoring the temporal consistency of degeneration and timing of vision information introduction. In the case of a continuously moving platform, degeneration typically manifests as segments, which can lead to significant issues for SLAM performance. To tackle this, we propose a vision-assisted LiDAR-inertial SLAM based on multiple segments under a degenerate environment, which solves the degenerate problem on LiDAR SLAM in the way of segment-based optimization. The degenerate segments are identified using an eigenvalue-based degenerate detection method with a buffer mechanism. In addition, the visual re-projection residuals are enforced within degenerate segments, together with LiDAR odometry residuals and inertial odometry residuals, through a dual sliding window approach. Finally, considering the pose nodes within the degenerate segment as individual fixed nodes, a pose graph in the back end is applied to merge segments for final pose estimation. The experiments with an ablation study on both simulation and real-world data are implemented, and the result demonstrates the robustness of the proposed method, compared against state-of-the-art algorithms.

Object-based visual-inertial odometry (VIO) is applied broadly in location service for ground vehicles and robotics, but tends to be limited to research of pure-vision navigation due to huge object proposal error in dynamic scene. To make up for this limitation, this paper proposes an object-based visual-inertial odometry for ground vehicles in dynamic scene with the aid of LiDAR, which carries out an object detection refinement using bounding box height adjustment on the basis of a coarse frustum-based joint object detection of vision and LiDAR. In addition, we model Euclidean distance of anchored point between consecutive states and encode it as anchored residuals for final sliding window based optimization. Both ablation study and comparison against the state-of-the-art visual-inertial algorithms are implemented in UrbanNav dataset, Loco sequences and self-made data. The experimental results demonstrate that in dynamic scenes with multiple objects, our proposed odometry achieves a 20.7% improvement in localization accuracy, along with more intuitive object proposals and enhanced robustness.

Using the navigation signal transmitted by GNSS (global navigation satellite system), satellites for tide level monitoring comprise one of the important research fields of GNSS marine remote sensing. Regarding the problem that GNSS-MR (multipath reflectometry) technology only uses carrier SNR (signal noise ratio) data, resulting in the lack of SNR data for early CORS (continuously operating reference stations) stations, it is impossible to carry out tide level inversion. In this paper, a method of tide level inversion based on triple-frequency geometric ionospheric free combined-phase observations instead of SNR is proposed. The simultaneous interpretation of GNSS satellite observations from the sc02 station in Friday Harbor in the US is carried out and compared with the traditional GNSS-IR (interference and reflectometry) tide-inversion method. The experimental results show that the tide level inversion method proposed in this paper has the same tide level trend as the measured tide level trend. The accuracy evaluation shows that the RMSE value of tide level inversion is 15 cm and the correlation coefficient r is 0.984, which verifies the effectiveness of this method for tide level monitoring and expands the method of GNSS tide-level monitoring.