A Coarse-to-Fine 2D Lidar Localization Based on Monte-Carlo and Iterative Closest Point

The 2D Lidar localization system, which typically has stringent accuracy and stability requirements, is a pivotal component in the practical implementation of autonomous mobile robots(AMRs) in industrial settings. However, in complex settings such as long corridors, glass reflections, multiple occlusions, and other degradation features, 2D Lidar localization systems are often unable to achieve the ideal positioning accuracy of 10mm. Regrettably, most commercial solutions can only be implemented in simple situations. To address above issues, this paper proposes a cascaded 2D Lidar localization solution combined with NDT-MCL and PL-ICP, that can fully meet the robustness and accuracy demands of the positioning system on industrial sites in complex scenarios. The experimental results, assessed in both simulated and real environment, demonstrate the superiority of our proposed method compared to other algorithms in terms of both robustness and accuracy. The autonomous navigation control of algorithm achieves an accuracy of less than
$\pm 5\mathbf{mm}$
in repeated positioning in complex environments, and the smoothing accuracy of the driving trajectory reaches 25mm. Additionally, the single-frame computation speed on the embedded rk3399 platform reaches 27ms.