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During a SLAM survey, the scanner continuously estimates its position and orientation while building the point cloud. As the survey progresses, small positioning errors may accumulate along the scanning path, resulting in drift. In the final point cloud, this may appear as double walls, misaligned structures, or distorted corridors.

The H1 is our first SLAM-based device, and for many users it is also their first experience with SLAM workflows. During real-world use, users may occasionally encounter issues when processing color point clouds, typically related to data quality or scanning conditions.

When collecting point cloud data, many workflows start from geometric reconstruction. In practice, absolute coordinates can be introduced in different ways—for example, through ground control points during post-processing, or directly during data acquisition using RTK.