Trimble

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 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.

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.

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.

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.

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.