The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 347–354, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-347-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 347–354, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-347-2020

  12 Aug 2020

12 Aug 2020

A MARKER-FREE CALIBRATION METHOD FOR MOBILE LASER SCANNING POINT CLOUDS CORRECTION

B. Yang1, Y. Li1, X. Zou2, and Z. Dong1 B. Yang et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • 2Autopilot Business Center, Tencent, Beijing, China

Keywords: Mobile Laser Scanning, Point Clouds, Calibration, Feature Extraction, Global Optimization

Abstract. Mobile laser scanning systems (MLS) have been widely used in collecting three-dimensional point clouds for many applications, such as 3D mapping, road facilities inventory and high definition map. Although MLS is calibrated accurately to obtain precise locations of point clouds, it is still challenging to obtain precise locations of point clouds in the areas of GPS signal denied or narrow streets with high dense buildings, resulting in uneven position deviations of point clouds between the overlapping trajectory areas. In this paper, a marker-free calibration method is proposed to solve the above problems. The proposed method firstly partitions the trajectory into segments according to the error distribution while collecting the point clouds. Secondly, the features in each overlapped area are extracted and a kind of Locally Aggregated Descriptors are built for the matching. Thirdly, a coarse-to-fine pairwise point clouds alignment is applied on the overlapping areas. Finally, the global alignment of point clouds is fulfilled with minimizing the position deviations between the overlapping areas and the adjacent segments. The proposed method has been used to correct the point clouds from several different MLSs. Experiments show that this method automatically locates and corrects the uneven position deviations in terms of good robustness and efficiencies. Besides, it proves that the proposed method is also an easy-to-use tool for the automatic correction of MLS point clouds position and boresights.