Backpack LiDAR-Based SLAM With Multiple Ground Constraints for Multistory Indoor Mapping
Baoding Zhou, Haoquan Mo, Shengjun Tang, Xing Zhang, Qingquan Li
Abstract
High-quality 3D point cloud maps are essential for precise indoor environments modeling. However, constructing such maps in multi-storey indoor environments is challenging due to the presence of narrow non-structural spaces, such as staircases, corners, and corridors with similar textures. Simultaneous localization and mapping (SLAM) in these scenes is particularly difficult, as cumulative errors can lead to incorrect loop closures and drastic degradation in map quality. To address these challenges. This paper proposed a SLAM method base on multiple ground constraints pose optimization (MGCPO) which uses a backpack LiDAR system. The proposed method includes two novel modules. The first, a regression analysis-based scenarios recognition (RASR) module provides a reference for the construction of ground constraints. The second, based on different scene detection results, the MGCPO module constrains the sensor pose using the floor plane to reduce localization errors and effectively decrease loop closure detection errors. Qualitative experiments demonstrate that our proposed method outperforms state-of-the-art methods in challenging scenarios. Quantitative experiments show that our method achieves an error rate of just 1.06% using only LiDAR sensors.