Confidence Factor-Based Robust Localization Algorithm With Visual-Inertial-LiDAR Fusion in Underground Space

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.