Systems and methods for augmenting an inertial navigation system (INS) include outputting from the INS position information associated with the implement and adjusting the implement based upon a comparison of the position information of the implement and a desired position of the implement. The INS is periodically re-initialized using error estimates generated by a kalman filter as a function of position information from one or more positioning (or measuring) devices, such as a fan laser, an automatic total station (ATS), a GNSS receiver, or a ground based radio positioning system, to correct a drift of the position information that may be caused by inherent characteristics of the INS.

A self-position estimation method includes: detecting a relative position of each target existing around a moving body relative to the moving body; estimating a movement amount of the moving body; correcting the relative position on a basis of the movement amount of the moving body and accumulating the corrected relative position as target position data; detecting a slope amount of a traveling road of the moving body; selecting, from among the accumulated target position data, the target position data of one or more targets in one or more sections having a slope amount less than a threshold value; and collating the selected target position data with map information indicating positions of the targets on a two-dimensional map to estimate a present position of the moving body.

A navigation system for a dynamic platform includes an inertial navigation system (INS) unit for measuring, in real-time, linear accelerations and angular velocities of the dynamic platform, and determining, using dead reckoning, initial estimates of current poses of the dynamic platform based on a previous pose of the dynamic platform and the linear accelerations and angular velocities of the dynamic platform. The navigation system further includes an exteroceptive sensor for acquiring sequential images of an environment in which the dynamic platform is traveling, a simultaneous localization and mapping (SLAM) unit for estimating visual odometer (VO) pose changes of the dynamic platform using the sequential images, and a sensor fusion engine for determining estimates of current poses of the dynamic platform based at least in part on the initial estimates of current poses determined by the INS unit and the VO pose changes estimated by the local sub-map tracker.

A system and methods are disclosed for providing the location of a tunnel boring machine (TBM) by establishing of a plurality of known locations or “monuments”; from these monuments located at least on, over or within the TBM's start point, known in the art as a “pit”. The present invention provides among other things an integrated navigation system that provides real-time parametric guidance information to the TBM, relative to the tunnel origin, past course and current trajectory, while simultaneously employing a non-contact measuring system in concert with said origin and course information for the final provision of an as-built map of tunnel dimensions and centerline.

Various arrangements for limiting inaccuracy of dead reckoning are presented. Proximity data may be collected using a plurality of proximity sensors of a mobile device. A position of the mobile device may be determined in relation to a user using the proximity data. Whether to determine a location of the mobile device using a dead reckoning technique may be determined at least partially based on the position of the mobile device in relation to the user.

A navigation and location system is provided that can include an inertial measurement unit (IMU) comprising an in a strap down configuration and a global positioning system (GPS), and a control section that determines orientation of a range sensor aligned with one axis of the IMU with respect to the Earth based on a sequence of system orientation and location measurements from said IMU/GPS along a displaced path from a starting point (SP) to an activation point (AP). Remote geo-location of the object can be determined based on determined range and bearing to the object determined by rotating axis of at least one reference frame from the strap down IMU axis with gravity and aligning another axis with a line of longitude through the displacement path to determine true north then determining a azimuth or bearing angle between true north and the range sensor output axis oriented on the object.

Systems, apparatuses, and methods for bias determination and value calculation of parameters of a robot are disclosed herein. According to at least one exemplary embodiment, a bias in a navigation parameter may be determined based on a bias in one or more measurement units, wherein a navigation parameter may be a parameter useful to a robot to recreate a route such as, for example, velocity and the bias may be accounted for to more accurately recreate the route and generate accurate maps of an environment.

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.

A computer system for processing machine vision data and performing active control of a mobile platform includes a plurality of sensors configured to acquire inertial and positional data. The system further includes a first plurality of co-processors having a hardware logic configured to control the acquisition of the inertial data. A second plurality of sensors is configured to acquire image data related to the mobile platform. The system further includes a second plurality of co-processors having a hardware logic configured to control the acquisition of the image data. The system further includes state management logic to perform state management operations for the acquired inertial and positional data in a computer-readable memory. The state management is performed using a state vector. The state management logic coordinates sharing and updating the acquired machine vision data in a parallel fashion between the first and second co-processors.

Disclosed are methods, systems, devices, apparatus, computer-/processor-readable media, and other implementations, including a method that includes obtaining, at a mobile device, remote transmitter location estimates for one or more remote transmitters, obtaining, at the mobile device, a group of pedestrian dead-reckoning (PDR) measurements, obtaining, at the mobile device, a group of signal measurements for signals received from the one or more remote transmitters, and deriving at least one mobile device location estimate for the mobile device based on one or more inconsistency terms representative of inconsistency between different location solutions, for the at least one mobile device location estimate, computed using different data points from the remote transmitter location estimates for the one or more remote transmitters, the group of PDR measurements, and the group of signal measurements.