Reducing the error in terrestrial laser scanning by optimizing the measurement set-up

High spatial resolution and fast capturing possibilities make 3D terrestrial laser scanners widely used in engineering applications and cultural heritage recording. Phase based laser scanners can measure distances to object surfaces with a precision in the order of a few millimeters at ranges between 1 and 80 m. However, the quality of a laser scanner end-product, like a 3D model, is influenced by many different parameters, especially the relative object surface orientation and the local point cloud density. This paper introduces the notion of point cloud quality. The obtained point cloud is first segmented using a planar feature extraction segmentation. Each segment is subdivided into smaller patches of 20 × 20 cm. For each patch a patch quality parameter is determined, which incorporates the local point density and local point quality. By averaging the patch quality over the complete point cloud, a point cloud quality is derived. This paper demonstrates this approach in practice by comparing two scans of the same test room obtained from different stand-points. As a result, it is shown and analyzed that by simply moving the scanner by two meters, the quality of the point cloud can be improved by 25 %