The present disclosure relates to a positioning apparatus and method and a self-moving device. The positioning apparatus includes a first positioning module (101), a sensor module (102), and a processing module (103). The position of the positioning apparatus is determined according to the positioning result of the first positioning module (101) and the positioning result determined by using the sensor module (102) to measure the acceleration and the angle parameter and based on a pedestrian dead reckoning algorithm, and determine the boundary of the self-moving device. A pedestrian dead reckoning technology independent of an external environment is introduced during boundary positioning, and the pedestrian dead reckoning technology and other positioning technologies are integrated to establish a virtual boundary, so that positioning precision is high, and a precise boundary is established. In addition, it is not necessary to arrange a physical boundary, so that operations of a user are less complex.

Model based inertial navigation for a spinning projectile is provided. In one embodiment, a navigation system comprises: a strapdown navigation processor; a propagator-estimator filter, the processor inputs inertial sensor data and navigation corrections from the filter to generate a navigation solution comprising projectile velocity and attitude estimates; an upfinding navigation aid that generates an angular attitude measurement indicative of a roll angle; and a physics model performing calculations utilizing dynamics equations for a rigid body, the model inputs 1) projectile state estimates from the navigation solution and 2) platform inputs indicative of forces acting on a projectile platform, and outputs a set of three orthogonal predicted translational acceleration measurements based on the inputs; the filter comprises a measurement equation associated with the physics model and the upfinding navigation aid and calculates the navigation corrections as a function of the navigation solution, the predicted translational acceleration measurements, and attitude measurement.

A hybrid approach for using reference frames is presented in which a series of anchor frames is used, effectively resetting a global frame upon a trigger event. With each new anchor frame, parameter values for lane boundary estimates (known as lane boundary states) can be recalculated with respect to the new anchor frame. Triggering events may a based on a length of time, distance traveled, and/or an uncertainty value.

The disclosed subject matter includes a method and system for navigating an unmanned ground vehicle (UGV), that include: generating, based on the scanning output data, a first map comprising a first group of cells and characterized by a first size; generating, based on the scanning output data, a second map representing an area smaller than that of the first map comprising a second group of cells, which are characterized by a second size being smaller than the first size; wherein each cell in the first group of cells and the second group of cells is classified to a class selected from at least two classes, comprising traversable and non-traversable, wherein the second part at least partly overlaps the first part; navigating the UGV based on data deduced from crossing between cells in the first map and second map.

A method for determining the rotational rate of a movable member using an array of inertial sensors is provided. The method includes defining a hidden Markov model (“HMM”). The HMM represents a discrete value measurement of the rotational rate of the movable member. A transition probability of the HMM accounts for a motion model (linear or non-linear) of the movable member. An observation probability of the HMM accounts for noise and bias of at least one of the inertial sensors of the array of inertial sensors. A processor receives input from the array of inertial sensors. The processor determines the rotational rate of the movable member by solving for an output of the HMM using the input received from the array of inertial sensors. The processor may use a forward algorithm, a forward-backward algorithm, or a Viterbi algorithm to solve the HMM.

A method for box regularized particle filtering, used to predict a state of a system, is modified such that it can be implemented in a parallel manner. The modification concerns a step of redistributing state intervals and in particular a determination of a number of sub-intervals intended to replace each state interval. The method is particularly suitable for being implemented in a navigation system with measurement correlation, for example an aircraft navigation system using ground-correlation, and for being executed by a field-programmable or fixed-gate array circuit.

The present disclosure discloses a map creation method of a mobile robot, including establishing a rectangular coordinate system in a working area of the mobile robot; causing the mobile robot moving in a character shape in the working area; obtaining a key frame picture of the mobile robot at a point Pi and saving the picture and coordinates of the point Pi; obtaining a picture of the mobile robot at a point P′i, wherein an abscissa or ordinate of the point P′i is the same as that of the point Pi; extracting and matching features of the pictures acquired at the point Pi and the point P′i; calibrating coordinates of the mobile robot at the point P′i and data of the odometer and/or the gyroscope according to the matching result and saving them; and repeating the obtaining to the calibrating until the map creation in the working area is complete.

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.

A hearing device has an acceleration sensor that measure along three mutually perpendicular measurement axis. A movement of the hearing aid wearer is deduced from acceleration data of the acceleration sensor issued in an acceleration signal, a movement plane of the movement of the hearing aid wearer is derived from the acceleration data, a movement axis and a movement direction of the movement are ascertained from the acceleration data, and the presence of a rotational movement of the head is deduced on the basis of the movement plane, the movement axis and the movement direction. A direction of view probability distribution is created from the detected rotational movements, in particular on the basis of a yaw angle ascertained in the process. The direction of view probability distribution specifies a probability that the actual direction of view of the hearing aid wearer extends along an assigned angle.

A method includes calculating, based on a first coordinate transformation matrix and a measured value of a gyroscope that corresponds to the ith sampling moment, a second coordinate transformation matrix corresponding to the ith sampling moment, where the first coordinate transformation matrix is a constant matrix, the first coordinate transformation matrix is a coordinate transformation matrix between a sensor coordinate system and a foot coordinate system, the second coordinate transformation matrix is a coordinate transformation matrix between the foot coordinate system and a ground coordinate system, and i is an integer greater than 0, and calculating, based on the first coordinate transformation matrix and the second coordinate transformation matrix corresponding to the ith sampling moment, an attitude matrix corresponding to the ith sampling moment, where the attitude matrix is a coordinate transformation matrix between the sensor coordinate system and the ground coordinate system.