Loop-closure detection plays Vehicle Toys a pivotal role in simultaneous localization and mapping (SLAM).It serves to minimize cumulative errors and ensure the overall consistency of the generated map.This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments.Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the AGE-DEFYING DAY CREME fusion validation of visual and laser loops.
Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE).Furthermore, it boosts loop-closure detection efficiency by an average of 15.
5%, thereby effectively enhancing the positioning accuracy of odometry.