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<p>Model-based stationing refers to the process of registering a set of measurements to a model. Specifically, in the surveying context, this refers to the process of determining the station, <i>i.e.</i>, the position and orientation, of a total station given a user-provided building floor plan and a series of polar measurements. Traditional methods compute the station using a set of known control points. We propose an automatic workflow which uses a novel registration method that does not require any known control points in order to find the station with high accuracy. Our registration algorithm relies on angle and distance measurements only; therefore, it is not limited to modern image-assisted total stations. In addition, the proposed workflow comprises a modeling phase to deal with model inaccuracies and produces reliable and accurate results. Quantitative and qualitative tests on synthetic and real-world scenarios demonstrate the performance and robustness of our automatic workflow and registration method.</p>

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Automatic Model-Based Stationing: Robust Total Station Localization Without Known Control Points

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月1日

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  • Fernando Reyes-Aviles,
  • Thomas Gloor,
  • Clemens Arth

摘要

Model-based stationing refers to the process of registering a set of measurements to a model. Specifically, in the surveying context, this refers to the process of determining the station, i.e., the position and orientation, of a total station given a user-provided building floor plan and a series of polar measurements. Traditional methods compute the station using a set of known control points. We propose an automatic workflow which uses a novel registration method that does not require any known control points in order to find the station with high accuracy. Our registration algorithm relies on angle and distance measurements only; therefore, it is not limited to modern image-assisted total stations. In addition, the proposed workflow comprises a modeling phase to deal with model inaccuracies and produces reliable and accurate results. Quantitative and qualitative tests on synthetic and real-world scenarios demonstrate the performance and robustness of our automatic workflow and registration method.