Paper details

Title: Indoor localisation and location tracking in indoor facilities based on LiDAR point clouds and images of the ceilings

Authors: Ioannis Dardavesis, Edward Verbree, Azarakhsh Rafiee

Abstract: Obtained from CrossRef

Abstract. Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the reliability of the ceilings towards indoor localisation, by using components that are included in a simple mobile device. The choice of ceilings lies in their advantages, which include the incorporation of various characteristic components, as well as the absence of obstacles between them and the sensor. Indoor localisation is achieved based on LiDAR point clouds and images from RGB sensors of mobile devices. Additionally, this research involves location tracking of different users, to discover different movement patterns in an indoor facility. The proposed methodology revealed the robustness of the Coloured ICP algorithm for in-door localisation based on point clouds, both in terms of time efficiency and quality, while the combination of the SURF feature detector and SIFT descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergencies, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations. The captured point clouds of the ceilings can also be used as a reference to CAD and BIM models, to help the modelling of the existing utilities and their components in an indoor facility.

Codecheck details

Certificate identifier: 2023-010

Codechecker name: Nina Wiedemann

Time of codecheck: 2023-06-13 12:00:00

Repository: https://osf.io/8t3bh

Codecheck report: https://doi.org/10.17605/osf.io/8t3bh

Summary:

The paper is accompanied by a GitHub repository with front end and back end code of their indoor localization app. The code was not executable first, but installation instructions were added and file paths were fixed upon exchange with the authors. The results reported in the paper are partially reproducible with the provided code. The code mainly yields examples that are similar but not the same as in the paper, due to randomness in the algorithms. One script allows to reproduce a figure exactly, while other quantitative results (tables in the paper) can not be created with the code. However, given the instructions in the README and the example data, the repository can be very useful for researchers who want to apply the pipeline on their own data.

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© Stephen Eglen & Daniel Nüst

Published under CC BY-SA 4.0

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