Data quality-oriented scan planning for steel structure scenes using a probabilistic genetic algorithm

doi:10.1016/j.autcon.2024.105700

Data quality-oriented scan planning for steel structure scenes using a probabilistic genetic algorithm

2024-11-01

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DOI：10.1016/j.autcon.2024.105700

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Scan planning is often challenging particularly in steel structure scenes because of its complex shapes and occlusions. Meeting the requirements of data quality for the scan-to-BIM model is also another issue for accurate point cloud data acquisition. To address these issues, this study proposes a solution that determines an optimal number of scans and corresponding scan positions and parameters. Three primary steps include 1) extraction of feature points using a slicing cutting method and range images, 2) evaluation of data quality using visibility check and data density evaluation, and 3) determination of optimal scan configuration using a probabilistic genetic algorithm. In order to validate the proposed solution, a series of lab-scale experiments involving five case studies with different scenarios are conducted and the results show a similarity of 88.4% between simulation and actual experiments, demonstrating the feasibility of the proposed method for steel structure scenes with complex shapes and occlusions.

Keywords:

Scan planning

Steel structure scenes

Data quality

Data acquisition

Scan-to-BIM

Probabilistic genetic algorithm

Data quality-oriented scan planning for steel structure scenes using a probabilistic genetic algorithm

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