ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-5-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 117–123, 2020
https://doi.org/10.5194/isprs-annals-V-5-2020-117-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 117–123, 2020
https://doi.org/10.5194/isprs-annals-V-5-2020-117-2020

  03 Aug 2020

03 Aug 2020

ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

C. Wang1, Y. Dai1, N. Elsheimy2, C. Wen1, G. Retscher3, Z. Kang4, and A. Lingua5 C. Wang et al.
  • 1Fujian Key Laboratory of Sensing and Computing, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen 361005, China
  • 2University of Calgary, Canada
  • 3TU Wien - Vienna University of Technology, Austria
  • 4China University of Geosciences, Beijing, China
  • 5Polytechnic University of Turin, Italy

Keywords: Multi-sensor, Indoor, Benchmark Dataset, SLAM, BIM, Indoor Positioning

Abstract. In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project.