The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 621–626, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-621-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 621–626, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-621-2016

  15 Jun 2016

15 Jun 2016

THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES

N. Alsubaie and N. El-Sheimy N. Alsubaie and N. El-Sheimy
  • Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada

Keywords: Smartphones, Dense Matching, Mobile Mapping System, Closed Range Photogrammetry, Linear Features Constraints

Abstract. This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone’s motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.