Determining vehicle orientation based on GNSS signals received by three antennas that are logically combined into two pairs, with one antenna common for both pairs. GNSS receiver measures first carrier phase difference within each pair of antennas, represented as sum of an integer number of periods of the carrier frequency and a fractional part of the period. The fractional parts are used to compute orientation of the vector connecting the antennas phase centers within each pair, excluding integer ambiguity resolution. Vehicle attitude is calculated from the orientation of two non-collinear vectors with a common origin, measured by two pairs of antennas. Each antenna has an RF front end. All RF front ends, heterodynes, digital navigation processors of this receiver are clocked from one common clock oscillator. All carrier phase measurements of the three antennas are performed on a common time scale.

In one embodiment, a method for calculating a location of an autonomous driving vehicle includes receiving new global navigation satellite system (GNSS) data. The method further includes identifying a first previously estimated location from a plurality of previously estimated locations with a timestamp that is closest to the timestamp of the new GNSS data and identifying a second previously estimated location from the plurality of previously estimated locations with a most recent timestamp. The method further includes calculating a difference between the first previously estimated location and the second previously estimated location, adjusting the new GNSS data based on the difference; and calculating a current estimated location of the ADV based on the adjusted GNSS data.

The present invention relates to a navigation assistance method for a mobile carrier (1) comprising an inertial navigation unit (10) comprising at least one inertial sensor (12), in which, over a determined observation window, the following steps are implemented by an estimation unit (11) of the inertial navigation unit (10): (E10) parametrisation of a non-linear system configured to estimate a navigation state of the mobile carrier (1) over a given time interval at an iteration n as a function of a kinematic model and/or measurements acquired by at least one inertial sensor (12); (E20) linearisation of the system so that the system expresses a navigation state at iteration n as a function of the state at iteration n−1 and a correction to this navigation state, said system being initialised by a first a priori state; (E21) estimating, by a Kalman filter and stochastic cloning, a first correction of a navigation state at iteration n; (E22) estimating a second correction of the navigation state at iteration n by an information filter operating in reverse and stochastic cloning; (E30) determining a third correction by merging the first and second corrections; and (E40) correcting the navigation state at iteration n as a function of the third correction, the corrected state being used at iteration n+1.

A method of operating a vehicle includes navigating the vehicle along a path, collecting a set of navigation parameters of the vehicle from at least one of a sensor, a global positioning system, or an inertial reference system, determining a set of statistical uncertainties related to at least some navigation parameters in the set of navigation parameters, and associating a set of statistical weights to the at least some navigation parameters.

Provided herein is a system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a previous pose of a vehicle; acquiring one or more previous readings corresponding to one or more wheel encoders during the previous pose; acquiring one or more readings corresponding to one or more wheel encoders acquired after the previous pose; and adjusting the previous pose based on the one or more readings to obtain a current pose.

A method and apparatus for determining pose of a transmitter, and by extension the pose of one or more receivers, in an electromagnetic tracking system when the transmitter is allowed to move freely in the physical environment. The method operates in a system that incorporates two separate tracking technologies and an Inertial Measurement Unit, one determining the pose of a device relative to a global reference frame and another determining the pose of the same device relative to a local reference frame, thereby avoiding the latency problem and resulting inaccuracy of pose determinations of known prior approaches.

A vehicle position estimation method includes: acquiring time-series data of a parameter related to a vertical motion of a wheel while the vehicle is traveling; acquiring the parameter around the vehicle, as a reference parameter, from a parameter map indicating a correspondence relationship between the parameter and a position; estimating a vehicle position based on a comparison between the time-series data of the parameter and time-series data of the reference parameter. Meanwhile, road surface roughness around the vehicle in a lateral direction and a lateral position of the vehicle in a road are recognized by using a recognition sensor installed on the vehicle. When the road surface roughness is less than a threshold, a lateral position component of the estimated vehicle position is replaced with the lateral position recognized by using the recognition sensor.

Disclosed herein, in one aspect, is a system for determining depth within a borehole. The system can comprise a downhole device comprising at least one inertial sensor, at least one processor, and a memory in communication with the at least one processor. The memory can comprise instructions thereon that, when executed, cause the processor to: receive data from the at least one inertial sensor and store the data from the at least one inertial sensor in the memory with respective correlated time values. The system can further comprise a drill rig comprising at least one depth measurement device. The at least one depth measurement device can comprises a drill string position sensor that is configured to produce a measurement indicative of a length of a portion of a drill string removed from a borehole or a wireline sensor that is configured to determine a length of deployed wireline cable.

Method, apparatuses, and computer program products for automatically tracking a heading of an object. An example method comprising receiving, one or more internal measurement values which pertain to an object; determining an internal heading uncertainty value for each internal measurement value of the one or more internal measurement values; generating, using a probabilistic heading model, an estimated heading data object for the object based at least in part on the one or more internal measurement values; and providing the estimated heading data object to one or more associated user devices.

The information processing apparatus comprises an inertial sensor provided in a vehicle; a position and orientation estimation unit for performing estimation processing for a position and orientation of the vehicle by using an output from the inertial sensor; a movement state acquisition unit for acquiring a movement state of the vehicle; and a first weighting determining unit for determining a first weighting in relation to output information from the inertial sensor in the estimation processing of the position and orientation estimation unit based on the movement state that has been acquired by the movement state acquisition unit.