What question did this study set out to answer?

The aim is to enhance the accuracy of lidar-based SLAM in underground mining environments plagued by error accumulation.

Lidar-based Simultaneous Localization and Mapping with Connecting Traverse Constraints for Underground Mine Mapping

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Demonstrates improved mapping accuracy in underground environments with a novel lidar-SLAM method incorporating traverse constraints.

The aim is to enhance the accuracy of lidar-based SLAM in underground mining environments plagued by error accumulation.
Utilized lidar scans to extract edge and planar features through segmentation
Refined features with least-squares fitting
Integrated features into a factor graph framework for pose estimation
Introduced an external traverse connection mechanism to reduce trajectory drift
Proposed method achieved the lowest RMSE of 0.467 m compared to LOAM, LeGO-LOAM, S-LOAM, and F-LOAM
Error reductions of 32.6%, 7.7%, 88.5%, and 81.6% for the self-collected dataset
On public datasets, achieved RMSE values of 0.084 m and 0.558 m, with relative reductions of 88.6% and 94.0% respectively

Xu et al. (2026) studied this question.

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