An object pose measurement system based on MEMS IMU includes: an accelerometer, a magnetometer, a gyroscope, an object vector information calculation unit, and a rotation compensation unit; wherein the object vector information calculation unit is connected respectively to the accelerometer, magnetometer, gyroscope to receive respective measurement data and calculate object vector information; the rotation compensation unit is connected to the object vector information calculation unit to receive the object vector information, compute and output rotation compensated object vector information; wherein the rotation compensation unit performs quaternion rotation compensation computation and outputs the rotation compensated quaternion as the rotation compensated object vector information.

An attitude sensor system with automatic bias correction having a primary attitude sensor wherein the primary attitude sensor comprises at least one accelerometer and an auxiliary sensor system configured to automatically estimate a bias of the accelerometer of the primary attitude sensor such that the resulting error is removed from an output of the attitude sensor system.

There is proposed a method of estimating a navigation state with several variables of a mobile carrier according to the extended Kalman filter method, comprising the steps of:acquisition of measurements of at least one of the variables,extended Kalman filtering (400) producing a current estimated state and a covariance matrix delimiting in the space of the navigation state a region of errors, with the help of a previous estimated state, of an observation matrix, of a transition matrix and of the measurements acquired, the method being characterized in that it comprises a step (310, 330) of adjustment of the transition matrix and of the observation matrix before their use in the extended Kalman filtering in such a way that the adjusted matrices satisfy an observability condition which depends on at least one of the variables of the state of the carrier, the observability condition being adjusted so as to prevent the Kalman filter from reducing the dimension of the region along at least one non-observable axis of the state space, in which the observability condition to be satisfied by the adjusted transition and observation matrices is the nullity of the kernel of an observability matrix associated therewith and in which the adjustment comprises the steps of:calculation (301) of at least one primary basis of non-observable vectors with the help of the previous estimated statefor each matrix to be adjusted, calculation (306, 308) of at least one matrix deviation associated with the matrix with the help of the primary basis of vectors, shifting (330) of each matrix to be adjusted according to the matrix deviation associated therewith so as to satisfy the observability condition.

An object pose measurement system based on MEMS IMU is disclosed, comprising: an accelerometer, a magnetometer, a gyroscope, an object vector information calculation unit, and a rotation compensation unit; wherein the object vector information calculation unit connected respectively to the accelerometer, magnetometer, gyroscope to receive respective measurement data and calculating at least an object vector information; the rotation compensation unit connected to the object vector information calculation unit to receive the at least an object vector information, compute and output a rotated compensated object vector information; wherein the rotation compensation unit performing a quaternion rotation compensation computation and outputting the rotated compensated quaternion as a rotated compensated object vector information.

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.

This disclosure describes various techniques for use within a vision-aided inertial navigation system (VINS). A VINS comprises an image source to produce image data comprising a plurality of images, and an inertial measurement unit (IMU) to produce IMU data indicative of a motion of the vision-aided inertial navigation system while producing the image data, wherein the image data captures features of an external calibration target that is not aligned with gravity. The VINS further includes a processing unit comprising an estimator that processes the IMU data and the image data to compute calibration parameters for the VINS concurrently with computation of a roll and pitch of the calibration target, wherein the calibration parameters define relative positions and orientations of the IMU and the image source of the vision-aided inertial navigation system.

An inertial device having sensors operable to output accelerations in a horizontal and a vertical direction is disclosed that includes a detection unit configured to detect a turning point in a waveform representing the acceleration in the vertical direction with respect to time and to detect time at the turning point; a calculation unit configured to calculate a velocity in the horizontal direction using the acceleration in the horizontal direction in a predetermined period centering on the time at the turning point; a determination unit configured to determine whether the velocity is less than or equal to a threshold value; and an estimation unit configured to estimate a direction to which a target having the inertial device moves using the velocity in response to a determination by the determination unit that the velocity is less than or equal to the threshold value.

A method based on separating ambient gravitational acceleration from a moving three-axis accelerometer data for determining a driving pattern is presented. A server may receive telematics data originating from a client computing device and combine the telematics data. The server may estimate a gravitational constant to the combined telematics data and generate a function for pitch and a roll angle from the combined telematics data. The server may further determine a driving pattern using at least the pitch and the roll angle.

The present disclosure includes a vehicle state recognition unit configured to determine states of a vehicle using an acceleration value and an angular velocity value, a stopping state gravity vector calculation unit configured to calculate a stopping state gravity vector value, an accelerating state vehicle acceleration vector calculation unit configured to calculate a vehicle acceleration vector value, a vehicle pitch angle vector calculation unit configured to calculate a vehicle pitch angle vector value, and a vehicle pitch angle calculation unit configured to calculate a vehicle pitch angle using a corresponding vehicle pitch angle vector value.