The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume XLII-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 457–460, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-457-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 457–460, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-457-2018

  26 Sep 2018

26 Sep 2018

MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING

C. Wen1,2, Y. Xia1,2, Y. Lian1,2, Y. Dai1,2, J. Tan1,2, C. Wang1,2, and J. Li1,3 C. Wen et al.
  • 1Fujian Key Laboratory of Sensing and Computing for Smart City and the School of Information Science and Engineering, Xiamen University, Xiamen 361005, China
  • 2Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
  • 3Faculty of Environment, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Keywords: Indoor Mobile Laser Scanning, Point Clouds, Indoor Mapping, Calibration, GPS/GNSS-denied

Abstract. Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy.

This paper has been retracted.