Reducing spatial error in mobile laser scanning by real-time uncertainty visualization and human-machine interaction


Type
Conference Object
Change log
Authors
Burgoyne, Chris J 
Brilakis, Ioannis 
Abstract

Scanning is a key element for many applications in the AECO industry. It provides point clouds used for construction quality assurance, scan-to-BIM workflows and construction surveys. However, data acquisition using static laser scanners or photogrammetry methods is lengthy and requires even lengthier subsequent processing. A quick and apparent escape from this problem might be mobile mapping solutions mainly based on lidars. However, current hand-held scanners suffer from drift, skewing point clouds and thus, increasing their spatial error. In this paper, we present a novel, real-time and fully explainable method exploiting human-machine interaction to increase the correctness of produced point clouds. Our method progressively reconstructs the scanned scene and predicts the regions of a potentially high error with a 95% confidence level. The user can then revisit these parts of the scene, which adds additional constraints on the underlying probabilistic graphical model, thus reducing the drift and increasing the confidence in the correctness of these regions. We build a prototypic lidar-based mobile scanner, implement our method and test it in a case study. The results show that the areas identified with a relatively high spatial error indeed suffer from it, while predicted areas with relatively higher correctness do have a smaller spatial error.

Description
Keywords
4013 Geomatic Engineering, 46 Information and Computing Sciences, 40 Engineering
Journal Title
Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)
Conference Name
39th International Symposium on Automation and Robotics in Construction
Journal ISSN
2413-5844
Volume Title
Publisher
International Association for Automation and Robotics in Construction (IAARC)
Sponsorship
EPSRC (EP/V056441/1)
BP, GeoSLAM, Laing O'Rourke, Topcon, Trimble