Real-time low-drift global optimization for dynamic scene LiDAR SLAM localization

Abstract

To address challenges like global drift, unstable matching, and high computational cost in light detection and ranging simultaneous localization and mapping (LiDAR SLAM) under complex conditions, this paper proposes an improved algorithm based on the LeGO-LOAM framework. A Newton-optimized normal distributions transform (NDT) is integrated to improve point cloud registration by constructing a negative log-likelihood objective and optimizing pose estimation. Using initial pose information from LeGO-LOAM accelerates convergence and enhances system robustness. This work addresses the problem of insufficient adaptability of existing algorithms in real scenarios. By deploying an independently designed four-wheel omnidirectional mobile robot platform, a hybrid LiDAR SLAM framework is used for precise positioning and map construction in complex campus environments, successfully reducing the positioning error to the centimeter level. Experiments on the KITTI dataset show a 43.51% reduction in maximum localization error and a 30.83% decrease in average error. Field tests in real-world campus environments with pedestrians, bicycles, and vehicles demonstrate strong reliability, adaptability, and resistance to interference. Horizontal error was reduced by about 58.26%, lowering the average error from 4.60 m to 1.92 m. Although computational load increases, it is offset by using high-performance LiDAR and processors. The enhanced accuracy and drift reduction significantly outperform traditional methods. At critical time points such as 50 seconds and 100 seconds, the system achieved high-precision pose estimation and accurate environmental reconstruction.