Hierarchical Multi-Level Information Fusion for Robust and Consistent Visual SLAM

Vision-based simultaneous localization and mapping (SLAM) has been indispensable for autonomous vehicles. However, in challenging environments, such as sparsely textured parking lots, the state-of-the-art methods often fail to obtain sufficiently reliable features for robust localization and consistent mapping. This paper proposes a multi-level visual information hierarchical fusion SLAM framework based on a multi-camera system. High-level features like object edges are extracted by different segmentation methods. With the guidance of high-level information, low-level key points are selected. The hierarchical fusion structure consists of two parts. High-level visual information is loosely fused with the wheel encoder to estimate initial data association. Then, low-level feature points of multiple views are tightly fused to facilitate robust localization. With such a structure, a consistent map can be built even under the condition that one view is lost. In addition, the lost view can be reinitialized in the single map with other information instead of waiting for re-localization or creating additional maps. Real-time experiments in autonomous parking scenario validate the robustness and consistency of the proposed method.