Visual SLAM Combining Lines and Structural Regularities: Towards Robust Localization

Visual Simultaneous Localization and Mapping (VSLAM), which serves as the primary technique for locating autonomous vehicles, has gained tremendous development over the past few decades. While improving the robustness of VSLAM in structural scenarios in autonomous driving remains a challenge. The point-based method tends to have unsatisfactory robustness due to limited environmental information and low-level geometric representation. Given the above underlying disadvantages, numerous studies have been conducted to search for high-level geometric elements and structural properties. This survey summarizes different methods and their development processes to improve robustness. This survey provides a comprehensive overview of point-line combination VSLAM and performs detailed comparisons of varying line parameterizations. In addition, the applications of structural regularities and object-based information in the VSLAM system are presented in the following chapters. Finally, open issues in enhancing the robustness and future development directions are highlighted.