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.

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.

The present disclosure relates to positioning technology, and particularly to a positioning method a positioning device, and a robot. In which, the method includes: obtaining first location information of the target object at a current moment being predicted by an extended Kalman filter model at a last moment; obtaining second location information of the target object at the current moment being collected by a sensor; predicting third location information of the target object at the current moment through the extended Kalman filter model based on the first location information and the second location information; and determining an error value of the third location information under a preset constraint condition, and correcting the third location information according, to the error value to obtain final location information of the target object at the current moment.

Systems and/or devices for implementing a tracking device for tracking a position/location and orientation of an object are provided herein. The device comprises one or more sides that define a predetermined shape, and a plurality of inertial measurement units (IMU) mounted to the one or more sides of the predetermined shape. Each IMU is configured to detect movement of the object and generate inertial output data representing a position and/or orientation of the object. Each IMU includes a first sub-sensor and a second sub-sensor. Each IMU is positioned at a predetermined distance and orientation relative to a center point of the tracking device. The device also comprises a controller communicatively coupled to the plurality of IMUs, the controller configured to receive output data from each of the plurality of IMUs, and determine position/location and orientation of the object based on the received output data from the plurality of IMUs and known data points for the predetermined shape to eliminate drift from sensor data.

Embodiments are disclosed for indoor/outdoor detection using a mobile device, such as wearable computer (e.g., smartwatch). In an embodiment, a method comprises: receiving, by one or more processors of a wearable computer, wireless access point (AP) scan data, global navigation satellite system (GNSS) data and inertial sensor data; determining, by the one or more processors, a first state of the wearable computer based on the wireless AP scan data; determining, by the one or more processors, a second state of the wearable computer based on a comparison of the GNSS data and the inertial sensor data; and outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors based on the first and second states.

A dead-reckoning guidance system determines a vehicle-speed of the host-vehicle based on wheel-signals from the one or more wheel-sensors; determines a distance-traveled by the host-vehicle during a time-interval since prior-coordinates of the host-vehicle were determined; determines a heading-traveled of the host-vehicle during the time-interval since the prior-coordinates of the host-vehicle were determined; determines present-coordinates of the host-vehicle based on the distance-traveled and the heading-traveled; determines when the vehicle-speed is greater than a speed-threshold; determines when the heading-traveled differs from a cardinal-direction by both greater than a noise-threshold and less than an angle-threshold; and in response to a determination that both the vehicle-speed is greater than the speed-threshold and that the heading-traveled differs from the cardinal-direction by both greater than the noise-threshold and less than the angle-threshold, determines a coordinate-correction to apply to the present-coordinates, said coordinate-correction determined in accordance with the distance-traveled and the cardinal-direction.

An apparatus, a system and a method indirectly detect the operational state of a machine among a plurality of operational states and track the movement of a material through a plurality of machines.

An apparatus includes a coarse resolver configured to output coarse position signals indicative of a coarse position of a drive shaft of a motor. The apparatus also includes a fine resolver configured to output fine position signals indicative of a fine position of the drive shaft of the motor. The apparatus further includes a control circuit. The control circuit is configured to receive the coarse position signals from the coarse resolver and the fine position signals from the fine resolver and generate an initial position output, based on the coarse position signals, that indicates an initial position of the drive shaft. The control circuit is further configured to generate a subsequent position output, based on the fine position signals, that indicates a subsequent position of the drive shaft.

Methods of calibrating strapdown heading sensors and strapdown heading sensors are provided. The methods include compensating raw sensor data generated by sensors of an uncalibrated strapdown heading sensor to compensate for errors in an instrument frame of the strapdown heading sensor. The strapdown heading sensor is put in a target apparatus and output data is compensated to compensate for errors in an apparatus frame relative to the instrument frame. The strapdown heading sensors include a housing and a compass module having a first sensor configured to detect a magnetic field of the Earth and a second sensor configured to detect a gravitational force of the Earth. The first sensor and the second sensor are each passively isolated from bending and/or flexing of the housing such that an alignment between the first sensor and the second sensor is not disturbed due to the bending and/or flexing.

Embodiments of the disclosure provide systems and methods for updating an HD map. The system may include a communication interface configured to receive sensor data acquired of a target region by at least one sensor equipped on a vehicle as the vehicle travels along a trajectory via a network. The system may further include a storage configured to store the HD map. The system may also include at least one processor. The at least one processor may be configured to identify a plurality of data frames associated with a landmark, each data frame corresponding to one of a plurality of local HD map on the trajectory. The at least one processor may be further configured to jointly optimize pose information of the plurality of local HD maps and pose information of the landmark. The at least one processor may be further configured to construct the HD map based on the based on the pose information of the plurality of local HD maps.