Modeling outlier formation in scanning reflective surfaces using a laser stripe scanner

Scanning reflective surfaces using 3D laser scanners is a challenging task since reflective surfaces of complex geometry promote the formation of unwanted data outliers. These outliers are characterized with large measurement errors, which significantly deteriorate the quality of the scanned point cloud data. This paper experimentally investigates the formation of outliers in relation to scanning reflective surfaces using a commercial laser stripe scanner. Two outlier formation models are developed: mixed reflection and multi-path reflection. The undesirable specular reflections in both mixed reflection and multi-path reflection scanning situations cause multiple peaks in the image sensing arrays. The false image peaks are recorded and will eventually be observed as outliers because they are not part of the scanned object surface geometry. A series of scanning experiments have been conducted and the results confirm the validity of the developed outlier formation models. Potential applications of the developed models such as scan path evaluation and outlier filter design are also discussed.