Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 433-440, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-433-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 433-440, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-433-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

METRIC SCALE CALCULATION FOR VISUAL MAPPING ALGORITHMS

A. Hanel1, A. Mitschke1,2,3, R. Boerner1, D. Van Opdenbosch3, L. Hoegner1, D. Brodie2, and U. Stilla1 A. Hanel et al.
  • 1Photogrammetry and Remote Sensing, Technical University of Munich, Munich, Germany
  • 2ESG Elektroniksystem- und Logistik-GmbH, Fuerstenfeldbruck, Germany
  • 3Chair of Media Technology, Technical University of Munich, Munich, Germany

Keywords: Visual SLAM, Point Cloud, Pose Estimation, Scale Calculation, Object Detection

Abstract. Visual SLAM algorithms allow localizing the camera by mapping its environment by a point cloud based on visual cues. To obtain the camera locations in a metric coordinate system, the metric scale of the point cloud has to be known. This contribution describes a method to calculate the metric scale for a point cloud of an indoor environment, like a parking garage, by fusing multiple individual scale values. The individual scale values are calculated from structures and objects with a-priori known metric extension, which can be identified in the unscaled point cloud. Extensions of building structures, like the driving lane or the room height, are derived from density peaks in the point distribution. The extension of objects, like traffic signs with a known metric size, are derived using projections of their detections in images onto the point cloud. The method is tested with synthetic image sequences of a drive with a front-looking mono camera through a virtual 3D model of a parking garage. It has been shown, that each individual scale value improves either the robustness of the fused scale value or reduces its error. The error of the fused scale is comparable to other recent works.