Point cloud aided calibration of a combined geodetic survey instrument

A method of stationing a geodetic survey instrument, the method comprising:

• retrieving model data representing at least a part of the setting and comprising referencing data to an external coordinate system,
• acquiring dense point cloud data representing a topography of the setting,
• identifying a plurality cardinal features in the acquired point cloud data,
• merging the acquired point cloud data with the model data by finding correspondence between the identified cardinal features in the point cloud data and the respective cardinal features in the model data,
• segmenting the point cloud data into a plurality of fractions of the point cloud data,
• providing a local matching index for at least one fraction of the point cloud data, wherein the local matching index being based on:
• a weighted deviation of coordinate data of point cloud object points of the referenced point cloud data from the model data, and/or
• a weighted deviation of the identified cardinal features respectively in the point cloud data and in the model data
• reducing the point cloud data by excluding fractions of the point cloud data based on the local matching index and a deviation threshold,
• providing referenced point cloud data based on the merging of the reduced point cloud data with the model data,
• deriving referenced pose data for the survey instrument for performing subsequent operation of the single point and the point cloud measuring functionalities based on the referenced point cloud data,
• wherein the method further comprises
• automatically selecting at least one identified cardinal feature,
• targeting the at least one selected cardinal feature with the single point measuring functionality and generating single point measurement data comprising coordinate data of the at least one selected cardinal feature,
• updating the referenced point cloud data based on the single point measurement data comprising the coordinate data of the at least one selected cardinal feature,
• wherein the method further comprises:
• identifying a misaligned object, the misaligned object having a different orientation and/or position in the model data and in the referenced point cloud wherein:
• the updated referenced point cloud data having a first reference framework, and
• at least one excluded fraction representing the misaligned object having an improved local matching index, below a deviation threshold, with respect to a second reference framework differing from the first reference framework, and
• requesting, and on approval performing an update for the model data on the basis of a pose of the misaligned object in the updated referenced point cloud.

A survey instrument comprising:

• a single point measuring functionality;
• a point cloud measuring functionality;
• a communication interface;
• and
• a computing unit,
• wherein the point cloud measuring functionality:
• having a first measuring axis and being configured to emit a scanning beam along the first measuring axis,
• being configured to generate point cloud data comprising coordinate data of a plurality of point cloud object points by advancing the first measuring axis along a scanning pattern, wherein the point cloud object points representing a topography of a setting,
• wherein the single point measuring functionality:
• having a second measuring axis and being configured to emit a measuring beam along the second measuring axis, wherein the first measuring axis being referenced to the second measuring axis,
• being configured to target a single point target by aligning the second measuring axis to the single point target and to generate single point measurement data comprising coordinate data of the single point target,
• the communication interface being configured to receive model data representing at least a part of the setting, wherein the model data comprising referencing data to an external coordinate system,
• the computing unit being configured to identify cardinal features in the point cloud data and being configured to identify cardinal features in the model data,
• the survey instrument being configured to execute a stationing functionality comprising the automatic execution of the steps of:
• retrieving the model data representing at least a part of the setting and comprising referencing data to the external coordinate system,
• acquiring the point cloud data representing the topography of the setting by the point cloud measuring functionality,
• identifying a plurality of cardinal features in the acquired point cloud data by the computing unit,
• segmenting the referenced point cloud data into a plurality of fractions of the referenced point cloud data,
• providing a local matching index for at least one fraction of the referenced point cloud data, wherein the local matching index being based on;
• a weighted deviation of coordinate data of point cloud object points of the referenced point cloud data from the model data, and/or
• a weighted deviation of the identified cardinal features respectively in the referenced point cloud data and in the model data,
• reducing the referenced point cloud data by excluding fractions of the referenced point cloud data based on the local matching index, in particular wherein the local matching index exceeding a deviation threshold;
• merging the acquired point cloud data with the model data by finding correspondences between the identified cardinal features in the reduced point cloud data and the respective cardinal features in the model data by the computing unit,
• providing referenced point cloud data based on the merging of the acquired point cloud data with the model data,
• deriving referenced pose data for the survey instrument based on the referenced point cloud data, and
• utilizing the referenced pose data of the survey instrument for performing subsequent operation of the single point and the point cloud measuring functionalities,
• wherein the instrument is further configured to perform:
• identifying a misaligned object, the misaligned object having a different orientation and/or position in the model data and in the referenced point cloud, wherein:
• the updated referenced point cloud data having a first reference framework,
• at least one excluded fraction representing the misaligned object having an improved local matching index, below a deviation threshold, with respect to a second reference framework differing from the first reference framework, and
• graphically indicating the misaligned object, and
• requesting, and on approval performing an update for the model data on the basis of a pose of the misaligned object in the updated referenced point cloud.