Observation distribution modelling and closed-from precision estimation of scanned 2D geometric features for network design

Lichti, Derek D.; Pexman, Kate; Chan, Ting On

doi:10.1016/j.ophoto.2022.100022

Cited by 3 publications

(1 citation statement)

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“…Concerning measured accuracy, a large number of works deal with characterizing the measurement quality of indoor mapping systems ranging from static setups with terrestrial laser scanners (Soudarissanane et al, 2011;Calders et al, 2017;Lichti et al, 2022) and mobile laser scanning systems (Lehtola et al, 2017;Tucci et al, 2018;Salgues et al, 2020) over range cameras (Khoshelham and Oude Elberink, 2012;Hou et al, 2023) to even augmented reality entertainment hardware (Hübner et al, 2020) and smartphones (Díaz-Vilariño et al, 2022) which can be used for mapping indoor environments as well.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Geometry of an Indoor Scenario With Occlusions Based on Total Station Measurements of Wall Elements

Schmidt,

Volland,

Hübner

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Scan2BIM approaches, i.e. the automated reconstruction of building models from point cloud data, is typically evaluated against the same point clouds which are used as input for the reconstruction process. In doing so, the point clouds are often used as ground truth without considering their own inaccuracies. Thus, in this research, we investigate the manual creation of an accurate ground truth, with a process which takes into account the measurement accuracy as well as the modeling accuracy. Therefore we created a ground truth to an existing laser scan data with a total station, based on the assumption that a total station generally measures points more reliably. In addition, a manual selection and classification of points on the wall surfaces during the measurement, serves a reliable detection of the walls via plane fitting. This allows for the creation of a more reliable ground truth, which is determined by the intersection of the planes from corners and edges. The ground truth is aligned parallel to the axes of a local coordinate system. From MLS and TLS point clouds of the same building area, walls are manually classified and corners and edges are determined in a similar way to the total station. These TLS and MLS corners are registered to this ground truth using least squares optimisation at the vertices. The transformation thus determined is used to transform the laser scanning point clouds as well. The resulting errors in the corners and the whole point cloud are evaluated. We conclude that the standard deviation of wall surfaces alone isn’t sufficient to determine the quality of the reconstructed building model. Despite low measurement noise in single wall surfaces, deviations in the reconstructed room model may arise.

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