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Placing Control Points
  • 16 Feb 2024
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Placing Control Points

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Article Summary

A control point is a specific point on the floor or wall of the site. It has to be marked clearly and assigned a unique identifier.

The following are recommendations for control points:

  • Distribute control points evenly. Place them more than 5m apart from one another and, if possible, not in a straight line.

  • Place control points at the beginning and end of each dataset.

  • Include a minimum of three control points per dataset.

  • For automatic alignment without measured control points, neighboring datasets must have at least two control points in common in order to align the datasets to each other.

  • To improve map accuracy, place control points in corners, at the ends of corridors, and measure them. Place one control point at every corner of a long corridor. Details depend on the building layout.

  • Ground control points should be placed at least 50 cm from corners. One control point every 25m. Wall control points should be 160-180 cm high.

  • There is no fixed rule for how many control points to use within a dataset, it depends on the building layout and use case, but we have found placing control points every 25-50m is recommended.

  • For low complex halls, it is enough to calculate with 3 control points per 1000 m², while it can go up to 6 control points per 1000 m² for very complex, fragmented environments in which many small datasets are necessary.

Measured Control Points

When using measured control points to improve the absolute accuracy of the trajectory estimate, effective placement depends on the building layout. It is a trade-off between how accurate the mapping results need to be and the time spent on placing and measuring the control points.

In this type of building layout, we advise placing control points every 25 meters of the device's trajectory, at every corner and the ends of corridors, for high map and point cloud accuracy.

placing-control-points-image1

A diagram of a path  Description automatically generated with medium confidence


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