A location-estimating device comprises a processor configured to acquire first locational information representing the location of a moving object for each first information acquisition time and calculate a first estimated location, acquire second locational information representing the location of the moving object for each second information acquisition time and calculate a second estimated location, and when the first estimated location has been determined, calculate the first movement amount and moving direction between the first information acquisition time and current time, and estimate the location at the current time based on the first estimated location, first movement amount and moving direction, or when the first estimated location is not determined, calculate a second movement amount and moving direction between the preceding second information acquisition time and current time, and estimate the location at the current time based on the second estimated location, second movement amount and moving direction.

A method, a device and a system for safely and reliably performing real-time speed measurement and continuous positioning are provided. With the method, inertial navigation data from an inertial navigation signal source arranged in a train is detected, and correction data from a correction signal source is detected. In a case that no correction data is detected, a current speed and a current position of the train is determined based on the inertial navigation data, and in a case that the correction data is detected, the inertial navigation data is corrected with the correction data, and a current speed and position of the train are determined based on the corrected inertial navigation data. Therefore, even in the case that no correction data is detected, the real-time speed measurement and continuous positioning can be performed safely and reliably based on the inertial navigation data.

The disclosure relates to a tight-integrated navigation method assisted by Elman neural network when GNSS signals are blocked based on the tight-integrated navigation system model of the INS and GNSS. The dynamic Elman neural network prediction model is used to train the inertial navigation error model and the GNSS compensation model, so as to solve the problem of tight-integrated navigation when the GNSS signals are blocked. When the GNSS signals are blocked, the trained neural network is used to predict the output error of GNSS and compensate the output of inertial navigation, so that the error will not diverge sharply, and the system can continue to work in the integrated navigation mode. The low-cost tight-integrated navigation module is used, and the collected information is preprocessed to form the sample data for training the neural network to train the Elman neural network model.

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.

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.

Absolute position data (605) of a machine are determined for respective multiple times (t.1, t.3, t.5, t.8, t.11), and odometry position data of the machine are also determined. A pose graph (661) is generated, wherein edges (672) of the pose graph (661) correspond to the odometry position data, and nodes (671) of the pose graph (661) correspond to the absolute position data (605). The pose graph (661) is optimized to obtain an estimated position. Optionally, the odometry can also be estimated. A driver assistance functionality of the machine, for example a motor vehicle, can be controlled optionally on the basis of the estimated position. For example, the driver assistance functionality can relate to autonomous driving.

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 for determining the position of a vehicle is disclosed. GNSS signals from a global satellite navigation system are received by a receiving device). A vehicle velocity is detected; and a check is carried out as to whether the detected vehicle velocity falls below or exceeds a threshold value. After the vehicle velocity falls below the threshold value, the position of the vehicle is determined on the basis of a first calculation method. After the vehicle velocity exceeds the threshold value, the position of the vehicle is determined on the basis of a second calculation method. Both calculation methods include filtering the GNSS signals by a fusion algorithm. The calculation methods differing by input variables of the fusion algorithm.

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.

A mobile robotic device has a motion sensor assembly configured to provide data for deriving a navigation solution for the mobile robotic device. The mobile robotic device temperature is determined for at least two different epochs so that an accumulated heading error of the navigation solution can be estimated based on the determined temperature at the at least two different epochs. A calibration procedure is then performed for at least one sensor of the motion sensor assembly when the estimated accumulated heading error is outside a desired range.