Disclosed embodiments provide a technical improvement for providing visualization of vehicle speed via a speedometer in an instrument cluster of a vehicle/driver interface of a transportation vehicle by providing multiple indicia of vehicle speed and rate of change of speed in a manner that connotes vehicle motion so as to enhance the driving experience for the driver and improve safety.

Disclosed embodiments provide a technical improvement for providing visualization of vehicle speed via a speedometer in an instrument cluster of a vehicle/driver interface of a transportation vehicle by providing multiple indicia of vehicle speed and rate of change of speed in a manner that connotes vehicle motion so as to enhance the driving experience for the driver and improve safety.

Methods, techniques, apparatus, and algorithms are described for robustly measuring real-world distances using any mobile device equipped with an accelerometer and monocular camera. A general software implementation processes 2D video, precisely tracking points of interest across frames to estimate the unsealed trajectory of the device, which is used to correct the device's inertially derived trajectory. The visual and inertial trajectories are then aligned in scale space to estimate the physical distance travelled by the device and the true distance between the visually tracked points.

Methods, techniques, apparatus, and algorithms are described for robustly measuring real-world distances using any mobile device equipped with an accelerometer and monocular camera. A general software implementation processes 2D video, precisely tracking points of interest across frames to estimate the unsealed trajectory of the device, which is used to correct the device's inertially derived trajectory. The visual and inertial trajectories are then aligned in scale space to estimate the physical distance travelled by the device and the true distance between the visually tracked points.

Method and apparatus are disclosed for GNSS statistical speed calibration An example vehicle includes a wheel, a speed sensor for determining a first vehicle speed, an inertial sensor, and a processor. The processor may be configured for determining a second vehicle speed based on information from the inertial sensor and information from a satellite based system, determining that a difference between the first and second vehicle speeds is statistically significant, and responsively adjusting a value of the radius of the wheel.

Method and apparatus are disclosed for GNSS statistical speed calibration An example vehicle includes a wheel, a speed sensor for determining a first vehicle speed, an inertial sensor, and a processor. The processor may be configured for determining a second vehicle speed based on information from the inertial sensor and information from a satellite based system, determining that a difference between the first and second vehicle speeds is statistically significant, and responsively adjusting a value of the radius of the wheel.

The present disclosure provides wearable apparatus and method for calculating drift-free plantar pressure parameters for gait monitoring of an individual. Most conventional techniques use different kind of sensors placed in in-sole based wearable apparatus but are costly and not effective in calculating accurate plantar pressure parameters. The disclosed wearable apparatus uses off-the shelf piezoelectric sensors that are widely available in market with less cost. The drift-free plantar pressure parameters are calculated using drift-free static pressure data obtained by numerically integrating acquired dynamic sensor data from the piezoelectric sensors, using a LiTCEM correction mechanism. A 6-DOF Inertial Measurement Unit (IMU sensor) helps in isolating zero-pressure duration indicating when a foot of the individual is in air during a stride, while obtaining the drift-free static pressure data. The disclosed wearable apparatus calculate the drift-free plantar pressure parameters for long duration and facilitates monitoring walking patterns of the individual.

The present disclosure provides wearable apparatus and method for calculating drift-free plantar pressure parameters for gait monitoring of an individual. Most conventional techniques use different kind of sensors placed in in-sole based wearable apparatus but are costly and not effective in calculating accurate plantar pressure parameters. The disclosed wearable apparatus uses off-the shelf piezoelectric sensors that are widely available in market with less cost. The drift-free plantar pressure parameters are calculated using drift-free static pressure data obtained by numerically integrating acquired dynamic sensor data from the piezoelectric sensors, using a LiTCEM correction mechanism. A 6-DOF Inertial Measurement Unit (IMU sensor) helps in isolating zero-pressure duration indicating when a foot of the individual is in air during a stride, while obtaining the drift-free static pressure data. The disclosed wearable apparatus calculate the drift-free plantar pressure parameters for long duration and facilitates monitoring walking patterns of the individual.

The present invention relates to a wind velocity measurement method, a wind velocity estimator and an unmanned aerial vehicle (UAV). The wind velocity measurement method includes: determining current wind resistance interference of a UAV by means of system identification based on flight data and attribute data of the UAV; and calculating a wind velocity of a flight environment of the UAV according to the wind resistance interference and the inherent wind resistance of the UAV. The method realizes the wind velocity measurement by identifying parameters based on the principle of system identification without a newly added wind velocity sensor and an external database. Therefore, not only hardware device costs are saved, but also an additional computing burden and a problem about real-time performance are avoided. The method is simple and requires low costs.

The present invention relates to a wind velocity measurement method, a wind velocity estimator and an unmanned aerial vehicle (UAV). The wind velocity measurement method includes: determining current wind resistance interference of a UAV by means of system identification based on flight data and attribute data of the UAV; and calculating a wind velocity of a flight environment of the UAV according to the wind resistance interference and the inherent wind resistance of the UAV. The method realizes the wind velocity measurement by identifying parameters based on the principle of system identification without a newly added wind velocity sensor and an external database. Therefore, not only hardware device costs are saved, but also an additional computing burden and a problem about real-time performance are avoided. The method is simple and requires low costs.