Perception sensors of a vehicle can be used for various operating functions of the vehicle. A computing device may receive sensor data from the perception sensors, and may calibrate the perception sensors using the sensor data, to enable effective operation of the vehicle. To calibrate the sensors, the computing device may project the sensor data into a voxel space, and determine a voxel score comprising an occupancy score and a residual value for each voxel. The computing device may then adjust an estimated position and/or orientation of the sensors, and associated sensor data, from at least one perception sensor to minimize the voxel score. The computing device may calibrate the sensor using the adjustments corresponding to the minimized voxel score. Additionally, the computing device may be configured to calculate an error in a position associated with the vehicle by calibrating data corresponding to a same point captured at different times.

Among other things, a user interface device has a sensor configured to detect, at a wrist of a human, nerve or other tissue electrical signals associated with an intended contraction of a muscle to cause a rapid motion of a finger. An output provides information representative of the nerve or other tissue electrical signals associated with the intended contraction of the muscle to an interpreter of the information.

This measurement device for measuring the angular velocity or acceleration of a two-wheel vehicle, is provided with a main detection unit which detects the three-axis angular velocity or three-axis acceleration, a support unit which can support the main detection unit on the body of the two-wheel vehicle, and a correction unit which cancels the lean of the body to the left and right in the main detection unit.

A sensor data processing system for an autonomous vehicle receives inertial measurement unit (IMU) data from one or more IMUs of the autonomous vehicle. Based at least in part on the IMU data, the system identifies an IMU data offset from a deficient IMU of the one or more IMUs, and generates an offset compensation transform to compensate for the IMU data offset from the deficient IMU. The system dynamically executes the offset compensation transform on the IMU data from the deficient IMU to dynamically compensate for the IMU data offset.

Pressure-based estimation of a mobile device altitude or calibration of a pressure sensor involves machines that determine if a reference-level pressure value based on one or more measurements of pressure from a network of weather stations should or should not be used to calibrate a pressure sensor of a mobile device or to estimate an altitude of the mobile device. If reference-level pressure value should be used, the reference-level pressure value is used to calibrate a pressure sensor of a mobile device or to estimate an altitude of the mobile device. If the reference-level pressure value should not be used, a trend in pressure is determined, an estimated reference-level pressure value based on the trend is determined, and the estimated reference-level pressure value is used to calibrate a pressure sensor of a mobile device or to estimate an altitude of the mobile device.

The present invention relates to a road vehicle localisation method based on magnetic landmarks. Said method is comprised by an offline phase and by an online phase. The offline phase is responsible for creating a reference landmark database comprised by a plurality of magnetic landmarks, wherein each magnetic landmark is associated to a path location data. The online phase is projected to match a current anomaly detected with a reference anomaly of the reference landmark database, in order to estimate the location of a vehicle based on the path location data of the correspondent reference landmark.

It is also described a system comprised by a sensor unit, a storage unit and by a processing unit, which is specifically programmed to operate according the road vehicle localisation method developed.

The disclosure relates to a method for determining correction values for a number of sensors of a traveling motor vehicle. The method being based on backward calculation. The disclosure further relates to a method for determining a position of a motor vehicle, using the correction values. The disclosure also relates to an associated electronic control device and to an associated non-volatile computer-readable storage medium.

The disclosure relates to a method for determining correction values for a number of sensors of a traveling motor vehicle. The method being based on backward calculation. The disclosure further relates to a method for determining a position of a motor vehicle, using the correction values. The disclosure also relates to an associated electronic control device and to an associated non-volatile computer-readable storage medium.

A method for correcting data sensed by a sensor includes: acquiring the data sensed by the sensor; establishing a surface equation based on an acquired data to performing surface fitting; and correcting the data sensed by the sensor with the surface equation. The method further includes: evaluating the acquired data sensed by the sensor to update fitting parameters of the surface equation so as to obtain an updated surface equation; and correcting the data sensed by the sensor with the updated surface equation. Parameter data sensed by the sensor can be corrected in real time, which reduces the influence of environmental factors on the parameter data sensed by the sensor and improves the accuracy of the data sensed by the sensor.

A method for correcting data sensed by a sensor includes: acquiring the data sensed by the sensor; establishing a surface equation based on an acquired data to performing surface fitting; and correcting the data sensed by the sensor with the surface equation. The method further includes: evaluating the acquired data sensed by the sensor to update fitting parameters of the surface equation so as to obtain an updated surface equation; and correcting the data sensed by the sensor with the updated surface equation. Parameter data sensed by the sensor can be corrected in real time, which reduces the influence of environmental factors on the parameter data sensed by the sensor and improves the accuracy of the data sensed by the sensor.