Subway Tunnel Mapping Based on Improved Point-LIO

In the typical degenerate environment of subway tunnels, feature absence can lead to increased localization errors. Therefore, existing research has focused on improving algorithm accuracy. This article specifically analyzes another reason for the increased errors and even divergence phenomena when applying existing SLAM algorithms in tunnel scenarios, specifically the introduction of fitting errors when calculating residuals within subway tunnels. In order to effectively address the mapping divergence problem in subway tunnel scenarios, this paper proposes an improved Point-LIO algorithm, which enhances the algorithm's robustness and accuracy by filtering out the point cloud of tunnel walls that are perpendicular to the wall surface. This paper validates the improved algorithm using real subway tunnel data. The results show that compared to the unimproved Point-LIO algorithm, the proposed method can successfully map in subway tunnel scenarios, with significantly reduced localization errors, demonstrating the advantages of the algorithm in stability and accuracy. The algorithm proposed in this paper provides a new solution to the SLAM mapping problem in subway tunnel scenarios, and is of great significance in promoting the application of SLAM technology in underground space navigation and localization fields.