An air data computer senses acceleration and rotational rate of an aircraft with an inertial sensor assembly of the air data computer. The air data computer determines first attitude information of the aircraft based on the acceleration and rotational rate sensed with the inertial sensor assembly. The air data computer receives second attitude information of the aircraft from a source external to the air data computer, and determines attitude correction values based on the first attitude information and the second attitude information. The air data computer applies the attitude correction values to the first attitude information to produce error-corrected attitude information that is output from the air data computer.

The invention provides a method for initial alignment of radar assisted airborne strapdown inertial navigation system. By calculating the slant distance and angular position between the radar and the airborne inertial navigation equipment, a nonlinear measurement equation for the initial alignment of the radar assisted inertial navigation system is obtained. The unscented Kalman filter algorithm is used to estimate and compensate the error amount of strapdown inertial navigation system to complete the initial alignment task. The significance of the present invention is to provide an in-flight initial alignment solution when the global positioning system is limited, which has fast convergence speed and high estimation accuracy and has high engineering application value.

The present disclosure provides a mobile robot gyroscope data correction method, device, and apparatus. The method includes obtaining laser radar data of a current frame, wherein a loop closure detection of the laser radar data of the current frame is successful; using an iterative closest point (ICP) algorithm to determine information of Y candidate estimated poses of the current frame according to initial estimated positions of Y key frames of the mobile robot, laser radar point sets of the Y key frames, initial estimated angles of the Y key frames, and a laser radar point set of the current frame of the mobile robot; determining a quantized value of each of the Y candidate estimated poses of the current frame according to the laser radar point set of each of the Y key frames and the laser radar point set of the current frame, wherein there is a maximum quantized value among the Y quantized values; and correcting the gyroscope data of the mobile robot according to the candidate estimated pose information corresponding to the maximum quantized value among the Y quantized values.

A method of controlling a mobile platform includes measuring a distance between the mobile platform and an object at each of a plurality of positions of the mobile platform, and determining a position of the object based on results of measuring the distance.

A method for decoupling an angular velocity in a transfer alignment process under a dynamic deformation includes: (1) generating, by a trajectory generator, information about an attitude, a velocity, and a position of a main inertial navigation system and an output of an inertial device, and simulating a bending deformation angle {right arrow over ()} between the main inertial navigation system and a slave inertial navigation system and a bending deformation angular velocity {right arrow over ()}.sub. by using second-order Markov; (2) decomposing the dynamic deformation into a vibration deformation and a bending deformation, and establishing an angular velocity model under the dynamic deformation of a wing; (3) deducing an error angle {right arrow over ()} between the main inertial navigation system and the slave inertial navigation system; and (4) deducing an expression {right arrow over ()} of a coupling error angular velocity, and applying that to an angular velocity matching process of transfer alignment to improve the precision of the transfer alignment.

Examples of systems and methods for locating movable objects such as carts (e.g., shopping carts) are disclosed. Such systems and methods can use dead reckoning techniques to estimate the current position of the movable object. Various techniques for improving accuracy of position estimates are disclosed, including compensation for various error sources involving the use of magnetometer and accelerometer, and using vibration analysis to derive wheel rotation rates. Various techniques utilize characteristics of the operating environment in conjunction with or in lieu of dead reckoning techniques, including characteristic of environment such as ground texture, availability of signals from radio frequency (RF) transmitters including precision fix sources. Navigation techniques can include navigation history and backtracking, motion direction detection for dual swivel casters, use of gyroscopes, determining cart weight, multi-level navigation, multi-level magnetic measurements, use of lighting signatures, use of multiple navigation systems, or hard/soft iron compensation for different cart configurations.

Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.

Techniques are disclosed for systems and methods for navigating mobile structures. The mobile structure may include a main attitude & heading reference system (AHRS) and one or more devices. The one or more devices may include a slave AHRS such as a gyroscope. Data may be transmitted from the main AHRS to the one or more devices through a network. Latency may be present in the transmission of data. As such, data from the slave AHRS may be used to determine changes in heading and/or attitude of the mobile structure to compensate for such latency. In addition, such data may be used to determine changes in wind direction and/or heading experienced by the mobile structure.

An Inertial Measurement Unit (IMU), method, and navigation system are disclosed. For example, the method includes receiving a plurality of sensor values from an IMU, loading the plurality of sensor values and a plurality of true sensor values into a deep learning algorithm, and training the deep learning algorithm to enhance the accuracy of the plurality of sensor values utilizing the plurality of true sensor values.

Systems and methods for determining and reducing drift in inertial navigation systems (INS). One method includes receiving images and drifted positions associated with a plurality of INS. The method includes detecting, from the plurality of images, a plurality of objects associated with the plurality of INS. The method includes determining, relative positions for the objects. The method includes generating a plurality of avatars, each having a virtual position, and associating each of the plurality of objects to one of the plurality of avatars. The method includes, for each of the INS, calculating a relative drift based on the relative position of the object and the drifted position of the INS. The method includes calculating a drift correction factor for at least one of the INS, and transmitting the drift correction factor to an electronic device associated with the INS.