Volume IV-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 13-20, 2018
https://doi.org/10.5194/isprs-annals-IV-1-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 13-20, 2018
https://doi.org/10.5194/isprs-annals-IV-1-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

DEVELOPMENT OF A PORTABLE HIGH PERFORMANCE MOBILE MAPPING SYSTEM USING THE ROBOT OPERATING SYSTEM

S. Blaser1, S. Cavegn1,2, and S. Nebiker1 S. Blaser et al.
  • 1Institute of Geomatics, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
  • 2Institute for Photogrammetry, University of Stuttgart, Germany

Keywords: Mobile Mapping, Indoor, Robot Operating System, ROS, SLAM, Sensor Orientation, Infrastructure

Abstract. The rapid progression in digitalization in the construction industry and in facility management creates an enormous demand for the efficient and accurate reality capturing of indoor spaces. Cloud-based services based on georeferenced metric 3D imagery are already extensively used for infrastructure management in outdoor environments. The goal of our research is to enable such services for indoor applications as well. For this purpose, we designed a portable mobile mapping research platform with a strong focus on acquiring accurate 3D imagery. Our system consists of a multi-head panorama camera in combination with two multi-profile LiDAR scanners and a MEMS-based industrial grade IMU for LiDAR-based online and offline SLAM. Our modular implementation based on the Robot Operating System enables rapid adaptations of the sensor configuration and the acquisition software. The developed workflow provides for completely GNSS-independent data acquisition and camera pose estimation using LiDAR-based SLAM. Furthermore, we apply a novel image-based georeferencing approach for further improving camera poses. First performance evaluations show an improvement from LiDAR-based SLAM to image-based georeferencing by an order of magnitude: from 10–13 cm to 1.3–1.8 cm in absolute 3D point accuracy and from 8–12 cm to sub-centimeter in relative 3D point accuracy.