Adaptive Laser Range Scanning using Quality Metrics

We present an approach to laser range scanning in which quality metrics are used to automatically reduce the number of measurements acquired from a scanner viewpoint in order to guide a minimally trained operator through the scanning process. As part of this approach we present improved versions of the orientation and reflectivity quality metrics, and introduce six new within-scan quality metrics: outlier, enclosed, resolvability, planarity, integration, and aliasing. These metrics are combined to generate a total within-scan quality metric for each measurement in the scan. The orientation, resolvability, reflectivity, and planarity quality metrics are used to divide the total field of view into regions based on their likelihood to produce useful measurements. A series of small high-density raster scans is then automatically generated to cover regions automatically identified as having a significant likelihood to produce useful measurements. All scans are then merged to generate a composite range image. The total number of measurements in the composite range image is minimized by merging statistically close measurements using a minimum variance estimator weighted by the total within-scan quality of each measurement.