Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

A vehicle position correction apparatus and a method thereof may include a learner that deep learns a model which predicts a position of a probe vehicle based on driving information of the probe vehicle traveling on a road, a communication device that receives driving information of a target vehicle from the target vehicle, and a controller that obtains a predicted position of the target vehicle based on the model on which the deep learning is completed and corrects an actually measured position of the target vehicle to the predicted position of the target vehicle.

Systems and methods for providing a health coaching message are disclosed. The method may include collecting electronic fitness data related to a fitness activity; transmitting the electronic fitness data; receiving the electronic fitness data; collecting second electronic fitness data; generating comparison data related to the electronic fitness data and the second electronic fitness data to determine a first ranking and a second ranking; and displaying the first ranking and the second ranking.

A method and apparatus for improving a dead reckoning estimate of a mobile unit is described. When an accurate position cannot be determined for a mobile unit, for example if GPS is unavailable, a dead reckoning estimate can be improved when two or more mobile units share their position estimates and the shared position estimates are used with either the range between the two units or knowledge that the units are within a threshold distance of each other to refine the position estimate of at least one unit.

Disclosed herein is a visual positioning method and apparatus, the method including: acquiring a video captured by an image sensor; determining visual positioning information respectively corresponding to a plurality of key image frames in the video; determining a capture pose transformation relationship between each of the plurality of key image frames according to inertial navigation information of the image sensor recorded when taking the video; performing, according to the visual positioning information corresponding to each of the plurality of key image frames, graph optimization processing on the visual positioning information corresponding to each of the plurality of key image frames by using the capture pose transformation relationship between each of the plurality of key image frames as an edge constraint; and determining, according to a result of the graph optimization processing, a visual positioning result of the image sensor when taking the video.

A sensor may be automatically calibrated during manufacture by providing a sensor processing unit having an integrated sensor, performing a check to determine if the integrated sensor has been previously calibrated upon a reset. When it has been determined the integrated sensor has not been previously calibrated, an automated calibration pattern may be imparted to the sensor so that a calibration parameter is determined.

Systems and methods utilize an inertial navigation sub-system configured to determine a plurality of relative positions of the navigation system from a reference position based on the determined speed and/or acceleration and/or direction of the navigation system and/or changes thereto; and a position estimator sub-system configured to estimate the absolute position of the navigation system based on at least two received signals. A first track is defined based on the plurality of relative positions determined by the inertial navigation sub-system during a period of time and a second track is defined based on the plurality of estimates of the absolute position by calculating a best fit using the plurality of estimates of the absolute position, where the second track approximates a same shape as the first track.

An electronic apparatus is provided. The electronic apparatus includes an acceleration sensor, a gyro sensor, a geomagnetic sensor, and a processor configured to compare geomagnetic data of the geomagnetic sensor and gyro data of the gyro sensor and correct the gyro data, determine a first value based on a principal component analysis (PCA) of acceleration data of the acceleration sensor, and determine a second value based on a PCA of the gyro data, and estimate a moving direction of the electronic apparatus based on the first value and the second value.

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

A relative navigation system comprising of a pair of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) units that communicate to provide updated position, velocity and attitude information from a master to a rover. The rover unit produces a carrier based solution that enables the system to reduce the uncorrelated low latency position error between the master and the rover units to less than 50 cm.