Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms

In recent decades, the use of terrestrial laser scanners has become the principal method for metric data collection in architecture. However, there are no systematic procedures in place to plan the data capture process. This means that the obtaining tasks of the clouds of points are based either on operator experience, or on the overlap register that grants a complete acquisition. In both cases, data redundancy represents a significant percentage, which forces subsequent filtration or point removal. This work describes the design and development of an automated methodology, based on genetic algorithms, for the selection of a set of positions from which to execute the data capture process.& nbsp; The algorithm designed herein is applied to a variety of cases, thereby attaining the best station-positioning network for data collection, which maximizes coverage and minimizes overlap between clouds of points.

Automated summary

In recent decades, terrestrial laser scanners have become the primary method for metric data collection in architecture. However, there is currently a lack of systematic procedures in place for planning the data capture process. This research introduces an automated methodology based on genetic algorithms to optimize the data capture process and minimize data redundancy.