A REPRODUCIBLE APPROACH TO ESTIMATE INDOOR SPACE AREA ON A HANDHELD LIDAR DATASET USING DBSCAN

Abstract. Research on handheld LiDAR data has recently been proliferated due to the emergence of digital twins and indoor mapping. However, most of the existing studies cannot be reproduced in another computational environment. Computational reproducibility requires data, code/software, and computational environment (e.g. versions, settings, etc.) to be openly available. Although there are an increasing number of researches that contribute towards open data, there are still few studies investigating the remaining two aspects. One of the common tasks in digital twin research is the estimation of indoor space areas. This paper contributes to the computational reproducibility of estimating the area of indoor spaces on a handheld LiDAR dataset using the DBSCAN algorithm. The collected dataset -representing the Geomatics Engineering Department of Hacettepe University, code, and the computational environment was made openly available to satisfy the requirements of computational reproducibility. Three different experiments have been carried out: i) identification of the optimal DBSCAN parameter values for a single indoor space, ii) evaluating to what extent these values are applicable to other rooms, and iii) investigating the effect of room enter/exit times on the estimated room sizes. The main finding of this paper is that the simple consideration of an open-door, which reduces data collection time, the uncertainty of a wall’s coordinates, and imperfect choice of DBSCAN parameters, may substantially increase the estimated indoor space size ranging between approximately 40% to 300%. Consequently, relying solely on the DBSCAN algorithm for indoor space area estimation should not be considered as a valid approach.