A motor control apparatus for securing insulation performance includes: a temperature sensor detecting a temperature of a motor; an altitude detection means providing information on an altitude at which the motor is positioned; and a controller including a motor output map having a plurality of groups of map data in which motor output limit rates depending on temperatures of the motor are pre-stored for each preset altitude section, selecting one group of map data corresponding to the information on the altitude provided by the altitude detection means, and determining a motor output limit rate by applying the temperature detected by the temperature sensor to the selected group of map data.

A method can include receiving sensor data, receiving satellite observations, determining a positioning solution (e.g., PVT solution, PVA solution, kinematic parameters, etc.) based on the sensor data and the satellite observations. A system can include a sensor, a GNSS receiver, and a processor configured to determine a positioning solution based on readings from the sensor and the GNSS receiver.

A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

A system for tracking a position of a working edge on an implement of a construction vehicle includes a GNSS with an antenna. The GNSS unit is configured to determine a position of the antenna and a tilt and a heading of the GNSS unit. A mount is configured to couple the GNSS unit to a rigid member of the construction vehicle. The mount is configured to couple the GNSS unit to the rigid member so that the antenna is arranged in a known spatial relationship with a pivot point between the rigid member and the implement. A mobile controller is configured for wireless communications with the GNSS unit and an angle sensor that is configured to determine rotation of the implement. The mobile controller is configured to receive the position of the antenna, the tilt, and the heading from the GNSS unit, to receive the rotation of the implement from the angle sensor, and to determine coordinates of the working edge of the implement in a real world coordinate frame.

Method, apparatuses, and computer program products for automatically tracking a heading of an object. An example method comprising receiving, one or more internal measurement values which pertain to an object; determining an internal heading uncertainty value for each internal measurement value of the one or more internal measurement values; generating, using a probabilistic heading model, an estimated heading data object for the object based at least in part on the one or more internal measurement values; and providing the estimated heading data object to one or more associated user devices.

A navigation method applied to an intelligent vehicle includes obtaining a navigation request, where the navigation request includes location information of a start place and location information of a destination that are of a vehicle; and navigating the vehicle based on the location information of the start place, the location information of the destination, and a heading of the vehicle, where the heading of the vehicle is determined based on a traveling track stored before the navigation request is obtained.

In some examples, an unmanned aerial vehicle (UAV) may determine a first acceleration of the UAV based at least on information from an onboard accelerometer received at least one of prior to or during takeoff. The UAV may determine a second acceleration of the UAV based at least on location information received via a satellite positioning system receiver at least one of prior to or during takeoff. The UAV may further determine a relative heading of the UAV based at least in part on the first acceleration and the second acceleration, and may be directed to navigate an environment based at least on the determined relative heading.

A method comprises generating a map comprising day-time features and night-time features, wherein the position of night-time features relative to the day-time features is determined by at least one image captured during twilight. The invention also relates to a corresponding processing unit configured to execute such a method.

A method comprises generating a map comprising day-time features and night-time features, wherein the position of night-time features relative to the day-time features is determined by at least one image captured during twilight. The invention also relates to a corresponding processing unit configured to execute such a method.