Abstract: Systems, methods, software, and devices are disclosed herein for training a neural network using multiple images of a scene captured from different viewing directions. The training is expedited by first determining foreground pixels in each of the multiple images, and then selecting a non-uniform sample of pixels from each of the multiple images such that the foreground pixels are overrepresented in the non-uniform sample of pixels relative to background pixels. The neural network may then be trained using radiance values of voxels on rays propagating from each of the non-uniform sample of pixels along a corresponding viewing direction into the scene.

Abstract: The present disclosure discloses a system and a method for jointly controlling a vehicle and updating a map using multiple probabilistic filters. The method comprises collecting a sequence of measurements indicative of the state of the vehicle at different control steps. The method also comprises executing multiple probabilistic filters configured to jointly track a current state of the location of the vehicle represented by coordinates of the vehicle and a current state of the map represented by coefficients of a polynomial forming a spline fitting representation of the map. The method including determining the location of the vehicle based on a first weighted combination of current states of the location and updating the map based on a second weighted combination of current states of the map, such that the weights of the first weighted combination and the second weighted combination are different.

Abstract: Provided is a system for tracking a state of a receiver. The system receives a set of measurements collected over a period of time, indicative of a motion of the receiver. The system further processes set of measurements relating to the receiver with a probabilistic smoother to estimate a state trajectory of the receiver for the period of time, the set of measurements is associated with a probabilistic estimation model. The probabilistic smoother iteratively implements a smoothing process followed by a feedback refining process for at least some parameters of the probabilistic estimation model until a termination condition is met. The smoothing process updates the state trajectory based on the at least some parameters. The feedback refining process updates the at least some parameters to improve a metric indicative of a measurement likelihood. The system further renders the estimated state trajectory and at least some estimated model parameters.

Abstract: The present disclosure discloses a system and method for controlling motion of a vehicle. The method comprises collecting a signal indicative of objectives of the motion and a value of the disturbances, and minimizing an objective function subject to constraints defined by the objectives of the motion to produce optimized values of parameters of a sequence of splines. The method further comprises controlling the motion of the vehicle based on a model of differentially flat dynamics of the vehicle according to an optimal path defined by the optimized values of the parameters of the sequence of splines.

Abstract: A system for tracking positions of a target object comprises circuitry configured to control an illumination source to periodically emit illumination pulses for illuminating the target object. The illumination reflected from the target object is detected to estimate a sequence of modulo distances to the target object wrapped by a pulse repetition period. The circuitry unwraps the sequence of modulo distances subject to one or more continuity constraints to output a continuous trajectory of the target object.

Abstract: Systems, methods, software, and devices are disclosed herein for training a neural network using multiple images of a scene captured from different viewing directions. The training is expedited by first determining foreground pixels in each of the multiple images, and then selecting a non-uniform sample of pixels from each of the multiple images such that the foreground pixels are overrepresented in the non-uniform sample of pixels relative to background pixels. The neural network may then be trained using radiance values of voxels on rays propagating from each of the non-uniform sample of pixels along a corresponding viewing direction into the scene.

Abstract: A state of a device is tracked based on transmissions from a global navigation satellite system (GNSS) by executing multiple probabilistic estimators concurrently tracking the state of the device using at least two measurement models of different size connecting the GNSS measurement data with the state of the device. One estimator tracks the state of the device based on a first measurement model using the code measurements. Another estimator tracks the state of the device based on a second measurement model using the GNSS measurement data masked by a binary mask blocking a subset of GNSS measurement data of a subset of the satellite signals based on values of coefficients of the binary mask. The number of measurements in the first measurement model is smaller than the number of measurements in the second measurement model.

Abstract: A system and/or a method for controlling the movement of a vehicle in a constrained environment subject to a disturbed vehicle model including uncertainty on the dynamics governing the movement of the vehicle, collects a feedback signal indicative of a state of the vehicle and a setpoint for controlling the vehicle according to a task and determine a robust invariant set centered on the setpoint for the operation of the vehicle in an unconstrained environment using the disturbed vehicle model. The robust invariant set is inflated equally in all directions until a termination condition defined by the constraint environment is met to produce a safe invariant set enabling control of the operation of the vehicle according to the task while maintaining the state of the vehicle within the safe invariant set.

Abstract: To track the state of a device under control, an unbalanced probabilistic filter is executed over a sequence of control steps to track a change in the state of the device caused by the control steps. For each of the control steps, the probabilistic filter uses a prediction model subject to prediction noise to estimate the current state of the device and uses a measurement model subject to measurement noise to update the estimate of the current state of the device based on measurements of the state of the device. The probabilistic filter is unbalanced because, for at least some of the control steps, the probabilistic filter executes the measurement model more times than the prediction model.

Abstract: Embodiments of the present disclosure disclose a method and a system for tracking the position of the one or more moving objects. The method includes collecting GNSS measurement data of satellite signals transmitted from multiple satellites. The method further includes extracting values of a plurality of features from the GNSS measurement data. The method includes mapping the extracted values of the plurality of features to a source domain. The method includes classifying the mapped transformed plurality of features using a neural network. The neural network is trained over simulated data sampled from a source domain. The method includes identifying multipath measurements of the GNSS measurement data based on classification of the corresponding mapped transformed plurality of features. The method includes tracking the position of the one or more moving objects by processing identification of GNSS measurement data affected by multipath.

Abstract: A system for controlling motion of an articulated vehicle with one or multiple trailers is described. The system is configured to infer the state of a vehicle, determine a motion path with forward motions and backward motions for the vehicle based on the state, and formulate an optimal control problem for optimizing the motion path. The optimal control problem comprises an integral tracking error function indicating integral tracking error based on motion cusps relating to the motion path. The motion cusps indicate switching between forward motion and backward motion. The motion path is optimized over a prediction horizon based on solving the optimal control problem. A control command is generated for the vehicle based on the optimized motion path and a vehicle model, and the motion of the articulated vehicle is controlled based on the control command thereby changing its state.

Abstract: Provided is a system for tracking a state of a receiver. The system receives a set of measurements collected over a period of time, indicative of a motion of the receiver. The system further processes set of measurements relating to the receiver with a probabilistic smoother to estimate a state trajectory of the receiver for the period of time, the set of measurements is associated with a probabilistic estimation model. The probabilistic smoother iteratively implements a smoothing process followed by a feedback refining process for at least some parameters of the probabilistic estimation model until a termination condition is met. The smoothing process updates the state trajectory based on the at least some parameters. The feedback refining process updates the at least some parameters to improve a metric indicative of a measurement likelihood. The system further renders the estimated state trajectory and at least some estimated model parameters.

Abstract: The present disclosure discloses a system and method for controlling motion of a vehicle. The method comprises collecting a signal indicative of objectives of the motion and a value of the disturbances, and minimizing an objective function subject to constraints defined by the objectives of the motion to produce optimized values of parameters of a sequence of splines. The method further comprises controlling the motion of the vehicle based on a model of differentially flat dynamics of the vehicle according to an optimal path defined by the optimized values of the parameters of the sequence of splines.

Abstract: A server jointly tracks states of multiple vehicles using measurements of satellite signals received at each vehicle and parameters of the probabilistic distribution of the state of each vehicle. The server fuse states and measurements into an augmented state of the multiple vehicles and an augmented measurement of the augmented state subject to augmented measurement noise defined by a non-diagonal covariance matrix with non-zero off-diagonal elements, each non-zero off-diagonal elements relating errors in the measurements of a pair of corresponding vehicles. The server executes a probabilistic filter updating the augmented state and fuses the state of at least some of the multiple vehicles with a corresponding portion of the updated augmented state.

Abstract: Embodiments of the present disclosure disclose a method and a system for tracking the position of the one or more moving objects. The method includes collecting GNSS measurement data of satellite signals transmitted from multiple satellites. The method further includes extracting values of a plurality of features from the GNSS measurement data. The method includes mapping the extracted values of the plurality of features to a source domain. The method includes classifying the mapped transformed plurality of features using a neural network. The neural network is trained over simulated data sampled from a source domain. The method includes identifying multipath measurements of the GNSS measurement data based on classification of the corresponding mapped transformed plurality of features. The method includes tracking the position of the one or more moving objects by processing identification of GNSS measurement data affected by multipath.

Abstract: A system jointly estimates states of GNSS receivers moving in a region using measurements of a Global Navigation Satellite System (GNSS). The system clusters the GNSS receivers into different clusters subject to a constraint on an upper bound on each cluster and executes a set of probabilistic filters corresponding to the set of clusters to estimate the states of GNSS receivers in each cluster. Each probabilistic filter estimates the states of the GNSS receivers in a corresponding cluster by fusing the GNSS data collected from the GNSS receivers in the cluster to jointly reduce an estimation error of each of the GNSS receivers in the cluster. The DES updates the cluster assignments based on a measure of estimation error in the states of different GNSS receivers in different clusters.

Abstract: The present disclosure discloses a system and a method for controlling a device using a compound probabilistic filter. The method comprises collecting a sequence of measurements indicative of the state of the device at different control steps. Further, the method comprises executing iteratively a compound probabilistic filter configured to track the state of the device at each of the different control steps using the sequence of measurements to produce a sequence of states of the device corresponding to the sequence of measurements. Furthermore, the method comprises controlling the device using the tracked state of the device.

Abstract: A system jointly estimates states of GNSS receivers moving in a region using measurements of a Global Navigation Satellite System (GNSS). The system clusters the GNSS receivers into different clusters subject to a constraint on an upper bound on each cluster and executes a set of probabilistic filters corresponding to the set of clusters to estimate the states of GNSS receivers in each cluster. Each probabilistic filter estimates the states of the GNSS receivers in a corresponding cluster by fusing the GNSS data collected from the GNSS receivers in the cluster to jointly reduce an estimation error of each of the GNSS receivers in the cluster. The DES updates the cluster assignments based on a measure of estimation error in the states of different GNSS receivers in different clusters.

Abstract: A hybrid distributed estimation system (DES) jointly tracks states of a plurality of moving devices configured to transmit measurements indicative of a state of a moving device and an estimation of the state of the moving device derived from the measurements. The hybrid DES selects between the measurements and the estimations, and based on this selection activates different types of DESs configures to jointly track the states of the moving devices using different types of information. Next, the hybrid DES tracks the states using the activated DES allowing track the state by different DES at different instances of time.

Abstract: A probabilistic system for tracking a state of a vehicle using unsynchronized cooperation of information includes a probabilistic multi-head measurement model relating incoming measurements with the state of the vehicle. The first head of the model relates measurements of the satellite signals subject to measurement noise with a belief on the state of the vehicle, and a second head relates an estimation of the state of the vehicle subject to estimation noise with the belief on the state of the vehicle. A probabilistic filter of the system updates recursively the belief on the state of the vehicle based on the multi-head measurement model accepting one or a combination of the measurements of the satellite signals subject to the measurement noise and the estimation of the state of the vehicle subject to the estimation noise.