The problem of sparsity and degeneracy issues in LiDAR SLAM has been addressed by introducing Quatro++, a robust global registration framework developed by researchers from the KAIST. This method has surpassed previous success rates and improved loop closing accuracy and efficiency through ground segmentation. Quatro++ exhibits significantly superior loop closing performance, resulting in higher quality loop constraints and more precise mapping results than learning-based approaches. The study examines how global registration affects graph-based SLAM, focusing on loop closing. Compared to learning-based methods, Quatro++ is particularly effective at closing loops, improving loop constraints,…

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