Some embodiments include a method and apparatus which obtains sensor data associated with a vehicle. The sensor data includes three-dimensional acceleration data and three-dimensional rotation data, with respect to a sensor coordinate system. A gravity vector is obtained, and it is determined when the vehicle starts moving. Acceleration direction is used as a forward direction of the vehicle. It is determined from the rotation data when the vehicle changes direction, the rotation data indicating two different directions. The gravity vector is used to distinguish which is an upward direction by selecting directions the direction which has larger angle with respect to the gravity vector as the upward direction. The forward direction and upward direction are used to determine a rightward direction. The forward direction, upward direction and rightward direction represent the vehicle coordinate system. The orientation of the apparatus with respect to the orientation of the vehicle is determined.

A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional “single epoch” methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.

An inertia-based navigation apparatus and an inertia-based navigation method based on relative preintegration are provided. The inertia-based navigation apparatus includes: a first sensor detecting and outputting motion information about a moving body which is moving, based on a first coordinate system; a second sensor detecting and outputting inertia data about a translational acceleration and a rotational angular velocity related to the movement of the moving body, based on a second coordinate system; and a controller determining, at every first time, pose information about a position, a velocity and an attitude of the moving body in a reference coordinate system, based on the motion information and the inertia data.

A method of calibrating an inertial measurement unit, the method comprising: (a) collecting data from the inertial measurement unit while stationary as a first step; (b) collecting data from the inertial measurement unit while repositioning the inertial measurement unit around three orthogonal axes of the inertial measurement unit as a second step; (c) calibrating a plurality of gyroscopes using the data collected during the first step and the second step; (d) calibrating a plurality of magnetometers using the data collected during the first step and the second step; (e) calibrating a plurality of accelerometers using the data collected during the first step and the second step; (f) where calibrating the plurality of magnetometers includes extracting parameters for distortion detection and using the extracted parameters to determine if magnetic distortion is present within a local field of the inertial measurement unit.

A posture estimation device includes an acquisition part acquires information of angular velocities and accelerations from a plurality of sensors that detects angular velocities and accelerations and that are attached to a plurality of locations on an estimation object, a conversion part that converts information acquired by the acquisition part into information of a standard coordinate system from a sensor coordinate system, an integrating part that calculates an orientation of a reference area of the estimation object as a part of a posture of the estimation object by integrating the converted angular velocities, and a correction part, assuming a representative plane passing through a reference area included in the estimation object, corrects the converted angular velocities of the reference area so that a normal line of the representative plane and an orientation of the reference area calculated by the integrating part approaches to directions that are perpendicular to each other.

A method of calibrating an inertial measurement unit, the method comprising: (a) collecting data from the inertial measurement unit while stationary as a first step; (b) collecting data from the inertial measurement unit while repositioning the inertial measurement unit around three orthogonal axes of the inertial measurement unit as a second step; (c) calibrating a plurality of gyroscopes using the data collected during the first step and the second step; (d) calibrating a plurality of magnetometers using the data collected during the first step and the second step; (e) calibrating a plurality of accelerometers using the data collected during the first step and the second step; (f) where calibrating the plurality of magnetometers includes extracting parameters for distortion detection and using the extracted parameters to determine if magnetic distortion is present within a local field of the inertial measurement unit.

A computer-implemented method of determining a location of a mobile device, the method comprising receiving inertial data generated at the mobile device, the inertial data comprising a plurality of samples taken at different times, segmenting the inertial data into pseudo-independent windows, wherein each pseudo-independent window comprises a plurality of the samples and wherein one or more initial states for each pseudo-independent window are treated as unknown, estimating a change in navigation state over each pseudo-independent window using the samples of inertial data, and summing the changes in the navigation states over the pseudo-independent windows so as to determine the location of the mobile device.

Inertial measurement method and apparatus for a mobile entity perform a filtering process for an angular velocity signal, an alignment process where an approximate initial attitude angle is calculated from acceleration and angular velocity signals and then precisely adjusted, an angular velocity/acceleration bias calculation process where angular velocity bias is calculated by subtracting Earth's angular velocity from the angular velocity signal and an acceleration bias is calculated by subtracting gravitational acceleration from the acceleration signal, an attitude angle calculation process where an angular velocity is calculated by subtracting Earth's angular velocity and the angular velocity bias from the angular velocity signal, and an attitude angle is calculated by integrating the angular velocity, a location movement amount calculation process where acceleration is calculated by subtracting the gravitational acceleration and the acceleration bias from the acceleration signal, and calculate a location movement amount by second-order integration for the acceleration.

An orientation identification method for identifying an orientation of a device installed by being mounted on a moving body includes: obtaining a certain amount of acceleration in three mutually orthogonal directions detected by an acceleration sensor included in the device; and identifying the orientation of the device expressed in a coordinate system from acceleration data indicating the certain amount of acceleration obtained in the obtaining, according to movement characteristics indicated by statistics of acceleration during movement of the moving body expressed in the coordinate system, the coordinate system including a gravitational acceleration direction as an axis.

Techniques provided herein are directed toward addressing these and other issues by providing robust means for initializing an accelerometer that can take place even while a vehicle is in motion. Specifically, linear acceleration and velocity data can be estimated from wheel speeds, and angular velocity can be estimated with a gyroscope. The vehicle's acceleration can then be computed from these estimates, and subtracted from a total acceleration measured by the accelerometer to determine gravitational acceleration, which can then be accounted for in subsequent measurements taken by the accelerometer. A vehicle velocity may also be determined based on the vehicle's estimated angular velocity and linear velocity. Embodiments may also employ techniques for translating measurements taken in one coordinate frame to another coordinate frame for estimate determination and/or outlier compensation.