Trimble Navigation Limited

Trimble Inc.

Applanix Corporation Inc.

Bruno M Scherzinger

Ulrich Vollath

James M Janky

Gregory C Best

Peter Glen France

Omar Soubra

Hongbo Teng

Sy Bor Wang

Joseph J Hutton

Blake Reid

Erik Lithopoulos

Nicholas Charles Talbot

Markus Glocker

A navigation system includes an IMU, a navigation estimator configured to estimate a current navigation solution based on (i) a previous navigation solution, and (ii) a specific force vector and an angular rate vector measured by the IMU, an RI sensor, an RI data preprocessor configured to perform an a priori transformation of RI data acquired by the RI sensor using the current navigation solution to obtain transformed RI data, an RI map database configured to retrieve a valid keyframe map based on the transformed RI data, and an RI filter manager (RFM) configured to construct a map registration cost gradient (MRCG) measurement based on (i) the transformed RI data, and (ii) the known position and the known orientation of the valid keyframe map. The navigation estimator is further configured to determine an absolute navigation solution based on at least (i) the current navigation solution, and (ii) the MRCG measurement.

Range image aided inertial navigation system (INS) with map based localization

A navigation system includes an IMU, a navigation estimator configured to estimate a current navigation solution based on (i) a last navigation solution, and (ii) a specific force vector and an angular rate vector measured by the IMU, an RI sensor, an RI data preprocessor configured to: perform a priori transformations of last RI data and current RI data to obtain transformed last RI data and transformed current RI data based on the last navigation solution and the current navigation solution, respectively, an RI filter manager (RFM) configured to construct a delta pose registration cost gradient (DPRCG) measurement based on (i) the transformed last RI data, (ii) the transformed current RI data, (iii) the current navigation solution, and (iv) the last navigation solution. The navigation estimator is further configured to determine an absolute navigation solution based on at least (i) the current navigation solution, and (ii) the DPRCG measurement.

Range image aided INS

A navigation system includes an IMU, a navigation estimator configured to estimate a current navigation solution based on (i) a last navigation solution, and (ii) a specific force vector and a n angular rate vector measured by the IMU, an RI sensor, an RI data preprocessor configured to: perform a priori transformations of last RI data and current RI data to obtain transformed last RI data and transformed current RI data based on the last navigation solution and the current navigation solution, respectively, an RI filter manager (RFM) configured to construct a delta pose registration cost gradient (DPRCG) measurement based on (i) the transformed last RI data, (ii) the transformed current RI data, (iii) the current navigation solution, and (iv) the last navigation solution. The navigation estimator is further configured to determine an absolute navigation solution based on at least (i) the current navigation solution, and (ii) the DPRCG measurement.

The invention relates to a method carried out by a navigation satellite system (NSS) receiver or a processing entity receiving data therefrom, for estimating parameters useful to determine a position. The NSS receiver observes NSS signals from NSS satellites over multiple epochs. A first filter, called 'timely estimator', and second filter, called 'precise estimator' and delayed with respect to the timely estimator, are operated. The estimators use state variables, and make use of NSS signals observed by the NSS receiver or information derived therefrom. The precise estimator further computes its state variable values based on observations that are not derived from NSS signals observed by the NSS receiver. The values of some of the state variables computed by the timely estimator are recurrently replaced by values from the precise estimator. A corresponding system is also disclosed.

Advanced navigation satellite system positioning method and system using delayed precise information

Method and apparatus are provided for image based positioning comprising capturing a first image with an image capturing device. Wherein said first image includes at least one object. Moving the platform and capturing a second image with the image capturing device. The second image including the at least one object. Capturing in the first image an image of a surface; capturing in the second image a second image of the surface. Processing the plurality of images of the object and the surface using a combined feature based process and surface tracking process to track the location of the surface. Finally, determining the location of the platform by processing the combined feature based process and surface based process.

Method and apparatus for image-based positioning

A quasi tightly coupled (QTC) aided INS (AINS) process has an inertial navigator system with a loosely-coupled AINS Kalman filter that constructs INS-GNSS position measurements, a GNSS position engine that computes a position fix from observables and an externally provided a priori position and position VCV matrix. An INS position seeding process in which the externally provided a priori position to the GNSS position engine is an antenna position computed from the INS position and attitude solution. An observable subspace constraint (OSC) process computes an OCS matrix that suppress the components of the GNSS position error due to a poor geometry in the GNSS position solution in the IG position measurement constructed by the AINS Kalman filter and that multiplies the OSC matrix and the IG position measurement and measurement model matrix to suppress uncorrected component of the GNSS position error in the IG position measurement and measurement model.

Quasi tightly coupled GNSS-INS integration process

A method and system obtains precise survey-grade position data of target points in zones where precise GPS data cannot be obtained, due to natural or man-made objects such as foliage and buildings. The system comprises position measurement components such as a GPS receiver, together with an inertial measurement unit (IMU) and an electronic distance meter (EDM) or an image capture device all mounted on a survey pole. The system and method obtains GPS data when outside the zone and uses the other position measurement systems, such as the IMU, inside the zone to traverse to a target point. The EDM or the image capture device may be used within or without the zone to obtain data to reduce accumulated position errors.

Land survey system

Control systems and methods that provide a high degree of vertical measurement accuracy for a body in motion are disclosed. The systems employ an inertial sensor system for vertical measurement and a Global Navigation Satellite System that includes multipath reduction or attenuation to provide corrected vertical information for a moving body to the inertial sensor system. The combination of these systems enables the maintenance of an accurate vertical position for said body.

Systems and methods for computing vertical position

A method of generating post-mission position and orientation data comprises generating position and orientation data representing positions and orientations of a mobile platform, based on global navigation satellite system (GNSS) data and inertial navigation system (INS) data acquired during a data acquisition period by the mobile platform, using a network real-time kinematic (RTK) subsystem to generate correction data associated with the data acquisition period, and correcting the position and orientation data based on the correction data. The RTK subsystem may implement a virtual reference station (VRS) technique to generate the correction data.

Post-mission high accuracy position and orientation system

A method and system obtains precise survey-grade position data of target points in zones where precise GPS data cannot be obtained, due to natural or man-made objects such as foliage and buildings. The system comprises a GPS receiver mounted on a survey pole, with an inertial measurement unit (IMU) and an electronic distance meter (EDM) also mounted on the pole. The system and method obtains GPS data when outside the zone and uses the IMU inside the zone to traverse to a target point, using the EDM to obtain data reduce accumulated errors which occur with the IMU.

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Richmond Hill, Canada

Bruno M Scherzinger