Automatic calibration of terrestrial laser scanners using intensity features

We propose an in situ self-calibration method by detecting and matching intensity features on the local planes in overlapping point clouds based on the Forstner operator. We successfully matched the intensity features from scans at different locations by feature matching on common local planes rather than on the rasterised grids of the horizontal and vertical angles adopted by the affirmed keypoint-based algorithm. The capability of extracting features from different stations offers the possibility of comprehensive scanner calibration, solving the disadvantage that the existing keypoint-based methods can only estimate the two-face-sensitive model parameters. The proposed algorithm has been tested with a high-precision panoramic scanner, Leica RTC360, using datasets from a calibration hall and a general working scenario. It has been shown that the proposed approach consistently calibrates the two-face-sensitive model parameters with the affirmed keypoint-based one. For the case of comprehensive calibration with the offset estimated and some angular parameters separated where the previous keypoint-based one failed, the proposed algorithm achieves an accuracy of 0.16 mm, 2.7 & DPRIME; and 2.1 & DPRIME; in range, azimuth and elevation for the estimated target centres. The proposed algorithm can accurately calibrate two-face-sensitive and more comprehensive model parameters without any preparation on-site, for example, mounting targets.

Automated summary

We propose an in situ self-calibration method that matches intensity features on local planes in overlapping point clouds using the Forstner operator. The method successfully matches intensity features from scans at different locations by matching on common local planes rather than rasterised grids. The proposed algorithm achieves accurate calibration of model parameters without any on-site preparation.