Talks and Poster Presentations (with Proceedings-Entry):
W. Cheng, Y. Dai, N. El-Sheimy, C. Wen, G. Retscher, Z. Kang, A. Lingua:
"ISPRS Benchmark on Multisensory Indoor Mapping and Positioning";
Talk: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
Nice, France (invited);
- 2020-09-02; in: "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",
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.
Multi-sensor, Indoor, Benchmark Dataset, SLAM, BIM, Indoor Positioning
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.