Leica Geosystems

The invention relates to a reality capture device (1) for generating a digital representation of an environment (2). The device comprises a laser scanner (3) configured to carry out a scanning movement of a laser measurement beam (4). The reality capture device comprises an upper portion (8) and a lower portion (9). A laser scanner (3) and an imaging arrangement (7) are comprised by the upper portion (8). The upper portion (8) is fixed on the lower portion (9) such that it is rotatable about a support rotation axis (10). The reality capture device is configured to have two scanning modes. In a stationary scanning mode the upper portion rotates about the support rotation axis relative to the lower portion and the laser scanner is set to move the measurement beam only relative to one of its two rotation axes. In a dynamic scanning mode, the laser scanner is set such that the measurement beam (4) is moved relative to both of the two rotation axes (5, 6), whereas the upper portion (8) is configured to be fixed on the support rotation axis (10) such that it does not rotate about the support rotation axis (10) relative to the lower portion (9).

The invention pertains to a computer-implemented method (100) for characterizing a macrotexture of a ground surface, the method comprising receiving (110) 3D point cloud data of a scene, the scene comprising one or more ground surfaces, the 3D point cloud data comprising a plurality of ground points related to the one or more ground surfaces, selecting (130) a sampling unit of one of the ground surfaces, performing plane fitting (140) for fitting one or more planes, each plane representing a portion of the sampling unit, wherein the plane fitting is based on ground points related to the portion represented by the respective plane, calculating (150) a set of roughness indices, each roughness index characterizing the macrotexture of the sampling unit, wherein the calculation of each roughness index is based on distance values of ground points related to the respective portions, the distance values indicating a distance of the respective ground point relative to the respective plane, generating (160) output based on the set of roughness indices, and providing (170) a graphical representation of the output in a graphical user interface.

The present invention relates to a computer-implemented method 1 for generating a parametric model 13 of a physical structure 36 based on point cloud data, the point cloud data describing a point cloud comprising points 14 acquired in an environment 34 in which the physical structure 36 is located, wherein the physical structure 36 comprises a plurality of parts, wherein the plurality of parts are arranged as an interconnected chain of parts that are physically connected, the method 1 comprising: acquiring the point cloud data 100, the point cloud data comprising geometry information of the environment 34; acquiring geometry information of a set of predefined geometric shapes related to the plurality of parts 200; performing a classification on the point cloud data 300 to detect parts of the plurality of parts in the point cloud data 301 based on the geometry information; assign to each detected part of the plurality of parts a set of points of the point cloud 302; and detect physical connections 5 of two or more detected parts 2, 3 of the plurality of parts 303; and generating 400 a parametric model 13 of the physical structure 36 based on the detected physical connections 5 of the two or more detected parts 2, 3 of the plurality of parts and the geometry information of the set of predefined geometric shapes, wherein the method 1 further comprises deriving geometric parameters 23, 24, 29 for each detected part 2, 3 of the plurality of parts from the geometry information of the set of predefined geometric shapes, wherein generating 400 the parametric model 13 of the physical structure 36 is further based on the derived geometric parameters 23, 24, 29 for each detected part 2, 3 of the plurality of parts, wherein at least one of the derived geometric parameters 23, 24, 29 of a preceding part 2 is used as a constraint for at least one of the geometric parameters 23, 24, 29 of a subsequent part 3 physically connected to the preceding part 2.

The invention pertains to a method of reducing noise in 3D data. The method comprises receiving 3D data that has been captured by a first laser scanner with a first resolution in a first environment. The method further comprises reducing noise in the 3D data by utilizing a noise reduction filter, wherein the noise reduction filter has filtering settings. The invention is characterized in that a trained machine learning model uses the filtering settings, wherein the filtering settings are learned by the machine learning model in a training process. The training process comprises multiple steps. Firstly, it comprises receiving training data from at least two laser scanners, the training data comprising a first set and a second set of 3D data that are structured as point-clouds. Secondly, it the training process comprises clustering the second set of 3D data to produce clustered data. Furthermore, the training process comprises training the machine learning model using the clustered data.

The present invention relates to a mobile target 1, provided to be aimed at and tracked by a geodetic measuring instrument 12, so that an actual absolute position 16 of the mobile target 1 is determinable related to an absolute position of the geodetic measuring instrument 12. The mobile target 1 is configured to be arranged on a motion unit 11, the motion unit 11 being provided to move through an environment 14. The mobile target 1 comprises a reflecting element 2, an inertial measurement unit (IMU) 3, a receiving unit 4, configured to receive first position data sent by the geodetic measuring instrument 12 with regard to the actual absolute position 16 of the mobile target 1, a computing unit 5, configured to continuously determine an actual relative position 15 and orientation of the mobile target 1 in the environment 14 based on the IMU data, wherein the actual absolute position 16 based on the first position data is used as a drift-corrector, and a data and/or power interface 6, configured to interact for data and/or power transmission with a corresponding data and/or power interface of the motion unit 11. Further, a housing 7 of the mobile target 1 comprises the reflective element 2, the IMU 3, the receiving unit 4, the computing unit 5 and the data and/or power interface 6.

The invention relates to a method and opto-electronic scanning measuring instrument (1) for in-line compensation of thermal influences on the opto-electronic scanning measuring instrument (1) by adapting a set of instrument's parameters associated with a defined reference thermal state of the instrument (1) in dependence on an actually measured thermal state of the instrument (1), sensed by thermal sensors (6a-6d) of the instrument (1), and calculating the respective object point coordinate based on the compensated parameter set.

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The invention relates to a Flying Sensor comprising an unmanned aerial vehicle (UAV) and at least one profiler being mounted on the UAV, wherein the at least one profiler comprises a base, a scanning unit configured for providing Light Detection And Ranging (LiDAR) data, the scanning unit mounted on the base and comprising a shaft carrying a deflector, the shaft being mounted in the scanning unit and rotatable about a rotation axis, a first transmitter configured for transmitting a first transmission beam via the deflector towards a setting, a first receiver configured for receiving a first reception beam reflected from the setting via the deflector, and an electric port configured for connecting the profiler to the UAV, said electric port comprising a data interface and a power interface, and wherein the UAV comprises a visual sensor for providing visual data, the visual sensor comprising one or more cameras, a pose sensor for providing pose data, the pose sensor comprising an Inertial Measuring Unit (IMU) and a Global Navigation Satellite System (GNSS) sensor or a Pseudo GNSS sensor, a computer configured to compute a 3D point cloud (P) of the setting based on the LiDar data and a Simultaneous Localisation and Mapping (SLAM) algorithm using the visual data and the pose data.