Trimble Patente

Described herein are systems, methods, and other techniques for selecting satellite signals for tracking at a GNSS receiver. A set of satellite signals are received via a GNSS antenna. Each of the set of satellite signals belongs to one of at least one GNSS constellation, operates within one of a set of frequency bands, and has one of a set of signal types. A correction signal is received at the GNSS receiver from a base station. The correction signal includes correction data for a subset of the set of satellite signals. While operating in accordance with a signal selection mode, the subset of the set of satellite signals are selected to be included in a set of active signals to be tracked by the GNSS receiver. Each satellite signal in the set of active signals is tracked using hardware elements of the GNSS receiver.

Described herein are systems, methods, and other techniques for detecting GNSS position outliers. GNSS observables are generated based on satellite signals received at a GNSS receiver. A position estimate is computed based on the GNSS observables. A set of code biases for the set of satellite signals are generated based on the position estimate and the GNSS observables. A set of code bias deviations for the set of satellite signals are computed as a difference between, for each of the set of satellite signals, a code bias of the set of code biases and a code bias model of a set of code bias models. A position error estimate is computed based on the set of code bias deviations.

Described herein are systems, methods, and other techniques for detecting and removing GNSS observable outliers. GNSS observables are generated based on satellite signals received at a GNSS receiver. A first position estimate is computed for the GNSS receiver based on the GNSS observables. A set of code biases for the set of satellite signals are computed based on the first position estimate and the GNSS observables. A set of code bias deviations for the set of satellite signals are computed as a difference between, for each of the set of satellite signals, a code bias of the set of code biases and a code bias model of a set of code bias models. A set of code multipath error estimates are computed based on the set of code bias deviations. A statistical outlier is identified from the set of code multipath error estimates.

Some embodiments of the invention pertain to methods for determining whether an object is in a liquid-surface-wave-influenced environment, such as for example in a ferry. The method comprises: acquiring (s10) measurement data, here referred to as "inertial data", from an inertial measurement unit (IMU) rigidly mounted with respect to the object; determining (s20) a value, here referred to as "autocorrelation function minimum's lag value", representing a lag at which an autocorrelation function of the acquired inertial data is minimum; and determining (s30) whether the object is in a liquid-surface-wave-influenced environment based on the autocorrelation function minimum's lag value. The disclosure also relates to, without being limited to, systems (1200), vehicles, computer programs, and computer program products.

Techniques are described for generating point clouds of a site. A laser scanner of an onsite device captures scan data of the site. A GNSS receiver of the onsite device captures GNSS position data. A georeferenced point cloud including a plurality of points is generated using the scan data and the GNSS position data. The georeferenced point cloud is segmented using a trained machine learning model by assigning the plurality of points to a set of classes including a noise class and one or more non-noise classes to produce a segmented point cloud. A portion of the segmented point cloud assigned to the one or more non-noise classes is wirelessly transmitted to an offsite device that is remote from the site. The portion of the segmented point cloud is stored at the offsite device in an accumulated point cloud for subsequent processing at the offsite device.

Some embodiments of the invention pertain to generating and transmitting information suitable for use in contributing to computing a positioning solution based on navigation satellite system (NSS) signals received by a NSS receiver, to minimize multipath and atmospheric errors in the resulting positioning solution so as to increase its accuracy. The method involves a plurality of reference stations 100, 200 comprising a primary reference station 100 and at least one secondary reference station 200. Systems and computer programs are also disclosed.

A computer implemented method is presented, the method comprising: collecting feature data comprising a plurality of features, each feature associated with a model of one or more models and a value; generating a machine learning model by performing feature selection on the feature data to select a chosen feature space comprising a subset of the plurality of features, wherein the subset of the plurality of features comprises one or more additional feature, wherein the value associated with each of the one or more additional feature in the chosen feature space is set to zero; training the machine learning model using the one or more values associated with the one or more features of the subset of the plurality of features; detecting a user initiated change affecting the chosen feature space. The method further comprises, in response to detecting the user initiated change affecting the chosen feature space: identifying one or more relevant features of the plurality of features; determining if at least one of the one or more relevant features is an unseen feature and performing either partial or full retraining based on the determination. The retrained model can then be applied in classifying the feature data.

Image data representing a two-dimensional image is received. Symbols are extracted from the image data using a detection service. The symbols are encoded into representation vectors, each representing distinct symbols. A labeled two-dimensional image is generated by (i) performing a comparison between a representation vector and representation vectors of symbols included in a library of known symbols, (ii) in accordance with determining that the corresponding representation vector exceeds a threshold similarity with a particular representation vector, applying a first label to the symbol, wherein the first label is a label of a first symbol that is represented by the particular representation vector, and (iii) in accordance with determining that the corresponding representation vector does not exceed the threshold similarity with representation vectors, applying a second label to the symbol. The labeled two-dimensional image is provided for facilitating a real-world operation involving the two-dimensional image.

Described herein are systems, methods, and other techniques for localizing an interference signal relative to a GNSS receiver. A method includes collecting a first phase difference measurement using first and second antennas and a first angle measurement using an inertial sensor. The method includes collecting a second phase difference measurement using the first and second antennas and a second angle measurement using the inertial sensor. The method includes searching a phase difference map using the first and second phase difference measurements and an angle offset between the first and second angle measurements to identify a compatible set of phase differences in the phase difference map. The angle of arrival of the interference signal is estimated based on indices of the compatible set of phase differences in the phase difference map.

There is provided a computer-implemented method for identifying a target object in a 3D point cloud. The 3D point cloud representing a real-world scene, and the method comprise obtaining plurality of 2D frames of the 3D point cloud depicting the target object at a respective plurality of different angles, identifying the target object in each of the plurality of 2D frames and based on identifying the object in each of the plurality of 2D frames, identifying a set of points in the 3D point cloud that represent the target object.

A method for generating a point cloud from an image of a scene, the method comprising: obtaining image data representing an image of a scene as captured by an image sensor; generating, based on the image data, an estimated point cloud comprising a plurality of estimated point positions corresponding to features of the scene, by using a machine-learning model trained to estimate point positions from such image data; selecting one or more features of the scene; obtaining, a measured point cloud comprising a plurality of measured point positions measured using electronic distance measurement, said plurality of measured point positions comprising at least respective measured point positions corresponding to the selected one or more features; comparing respective ones of the estimated point positions that correspond to the selected one or more features, and the respective ones of the measured point positions that correspond to the selected one or more features; determining based on the comparison, a transformation that can be applied to the plurality of estimated point positions, such that the respective ones of the estimated point positions more closely align with the respective ones of the measured point positions; and storing the transformation in a computer-readable memory. A further optional method comprises: obtaining second image data representing an image of a scene as captured by an image sensor; generating, based on the second image data, a second estimated point cloud comprising a plurality of estimated point positions corresponding to features of the scene, by using a machine-learning model trained to estimate point positions from such image data; and transforming the second estimated point cloud into a transformed point cloud using the transformation.

A surveying target 1 comprises cage 7 including a plurality of slats 9, and a target body 3 having a convex surface 5 positioned within the cage 7. The cage 7 is configured such that, in an elevational view of the surveying target 1, plural portions 17 of the surface 5 of the target body 3 are visible between adjacent slats 9. Moreover, in the elevational view, a perimeter 19 of the surveying target 1 is defined by the cage 7. The color of the slats 9 is different from a color of the surface 5 of the target body 3.

A computer-implemented method is disclosed, comprising: receiving, by one or more large language models (LLMs), system prompts causing the LLMs to perform a system method. The system method comprises: detecting the source language of a source text in natural language; determining, based on a user prompt, a target language; generating a first translation by translating the source text to the target language; generating a back translation by translating the first translation to the source language; comparing the back translation with the source text to identify inconsistencies; determining whether the inconsistencies exceed an error threshold; if the threshold is exceeded, generating a second translation of the source text to the target language; if not, outputting the first translation; processing, by the LLMs, the system prompts; receiving, by the LLMs, via a user device, a prompt containing the source text; and providing, by the LLMs, the first or second translation.

A surveying instrument (100) is provided. The surveying instrument comprises a distance measurement unit (102) and a first optical receiver (108). The distance measurement unit comprises a transmitter (104) and a detector (106). The transmitter is configured to emit light (L1) via a first optical path (OP1). The detector is configured to receive light (L2) via the first optical path. The first optical receiver is adapted to receive light (L3) via a second optical path (OP2). The first optical path includes a front lens (110), a beam splitting unit (112), and at least one first focus adjusting element (113). The at least one first focus adjusting element is configured to adjust a focus of the distance measurement unit. The second optical path includes the front lens, the beam splitting unit and at least one second focus adjusting element (117). The at least one second focus adjusting element is configured to adjust a focus of the first optical receiver. The beam splitting unit is configured to split the first optical path from the second optical path. The transmitter and the detector share the first focus adjusting element as a common focus adjusting element.

The present inventive concept relates to a system (1) comprising: a total station (10) comprising a center unit (100) mounted on an alidade (110) for rotation about a first axis (A1), wherein the alidade (110) is mounted on a base (120) of the total station (10) for rotation about a second axis (A2) orthogonal to the first axis (A1), whereby a sighting axis (A3) of the total station (10) is rotatable about a rotation point; and an optical calibration element (12) connectable to the total station (10) or a support (130) on which the total station (10) is installed; and wherein the optical calibration element (12) in a connected state is arranged such that, when the sighting axis (A3) is rotated about the rotation point to form a non-zero calibration angle (B) relative to a plane having a normal parallel to the second axis (A2), the center unit (100) is aimed toward a target (14) via the optical calibration element (12).

There is provided a surveying pole comprising a pole arrangement and a target internally arranged in the pole arrangement, the target being configured to reflect and/or refract light depending on a medium in contact with a surface of the target. A top unit mounted on the pole arrangement comprises a detector unit configured to emit light for propagation in the pole arrangement towards the target, and detect light reflected by the target. Said pole arrangement comprises a first portion forming a reservoir adapted to collect liquid, such that a collected liquid is in contact with said surface of the target. The surveying pole further comprises a control unit configured to, upon an absence of detected light reflected by the target, or upon a decrease in energy level of the detected light reflected by the target, generate a warning signal.

The present disclosure relates to a surveying instrument and to a method of operating a surveying instrument for tracking of a target including a plurality of light emitting elements and/or a plurality of light reflecting elements configured to emit and/or reflect light radially. The surveying instrument includes a tracking unit configured to adjust a sighting axis of said surveying instrument towards said target by maintaining a predetermined distribution of signal levels on a light sensitive detector. The method comprises obtaining an error function representative of a misalignment of said sighting axis with respect to a reference point in the target as a function of an orientation of the target relative to said surveying instrument, receiving information indicative of an orientation of said target relative to said surveying instrument while tracking said target, retrieving an error from said error function based on the received information, and compensating for the retrieved error.

A computer-implemented method for transmitting changes during collaborative model editing is disclosed. The method comprises running a modelling application configured to model physical structures as model objects and receiving a reference model object, wherein the reference model object is representative of at least part of a first model object. The method comprises creating a transmission object comprising at least one of a proxy model object comprising a modified version of the reference model object and a second model object. The method further comprises creating a reference transmission object, wherein the reference transmission object is representative of at least part of the transmission object, and transmitting the reference transmission object.

A data format embodied on a medium, wherein the data format defines an alignment for a linear construction infrastructure, wherein the data format comprises a series of points, each point defining a position on the alignment, wherein each point comprises: (i) a horizontal geographic coordinate of the point; (ii) a chainage number of the alignment at the point; and (iii) a horizontal direction of the alignment at the point.

The present disclosure provides a surveying instrument (100). The surveying instrument comprises a center unit (110) comprising an optical device (120) and a front lens (112). The optical device is arranged to emit and/or receive light through the front lens along an optical axis (OA) of the center unit. The surveying instrument further comprises an alidade (130) on which the center unit is rotatably arranged about a first axis (A1). The alidade comprises a tilt sensor (132) arranged along a second axis (A2). The second axis is orthogonal to the first axis. The alidade further comprises an optical arrangement (134) arranged to form an optical path (136) from a first side of the alidade to an aperture (138) arranged along the second axis at a second side of the alidade. The optical path bypasses the tilt sensor. The center unit is rotatable about the first axis to a predetermined rotational position (P1), in which the optical axis aligns with the optical path such that the optical device is arranged to emit and/or receive light through the aperture along the second axis.

Described herein are a system and method for compensating for antenna phase center and SNR variation in dual- and multi-antenna GNSS receivers. Satellite signals are received at a GNSS receiver having a plurality of antennas including a first antenna and a second antenna. GNSS measurements are collected based on the received wireless signals using the first and second antennas. A horizontal orientation of the plurality of antennas with respect to a transmitting satellite is estimated. One or both of an antenna phase center correction and an SNR correction are obtained from one or more calibration tables based on the horizontal orientation. A geospatial position of the GNSS receiver is estimated using the GNSS measurements and one or both of the antenna phase center correction and the SNR correction.

Described herein are systems, methods, and other techniques for performing conflation between target road polylines and source road polylines stored in GIS databases. A pairing of a target road polyline and a source road polyline is obtained. The target road polyline and the source road polyline each include a sequence of connected 2D or 3D vertices that form a continuous line. A similarity score is computed between the target road polyline and the source road polyline based on one or more similarity metrics. A conflation classification is assigned to the target road polyline, the source road polyline, or the pairing based on the similarity score. The conflation classification is one of a set of possible conflation classifications. A post-classification action is performed based on the conflation classification.

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Marking metadata during layout can be time intensive. A robotic device is used to mark out points at a construction site, and an augmented-reality device uses the points for alignment and provides metadata about the points to a user of the augmented-reality device as the user looks at the points.

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Techniques are described for calibrating an AR device having a camera, an antenna, an EDM device, and a display. First and second distances to first and second points are captured using the EDM device. First and second camera images containing the first and second points are captured using the camera. First and second 2D screen coordinates for the first and second points on the display are determined using the first and second camera images. First and second 3D surface points for the first and second points are computed based on the first and second 2D screen coordinates and the first and second distances. A position of the EDM device is computed using a 3D vector formed between the first and second 3D surface points. A camera-to-antenna offset is computed based on the position of the EDM device and a known antenna-to-EDM offset.

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