POSITION PROBABILITY DENSITY FUNCTION FILTER TO DETERMINE REAL-TIME MEASUREMENT ERRORS FOR MAP BASED, VISION NAVIGATION SYSTEMS

20220390237

17/340921

June 07, 2021

A navigation system for a vehicle comprises onboard sensors including a vision sensor, and an onboard map database of terrain maps. An onboard processer, coupled to the sensors and map database, includes a position PDF filter, which performs a method comprising: receiving image data from the vision sensor corresponding to terrain images captured by the vision sensor of a given area; receiving map data from the map database corresponding to a terrain map of the area; generating a first PDF of image features in the image data; generating a second PDF of map features in the map data; generating a measurement vector PDF by a convolution of the first PDF and second PDF; estimating a position vector PDF using a non-linear filter that receives the measurement vector PDF; and generating statistics from the estimated position vector PDF that include real-time measurement errors of position and angular orientation of the vehicle.

Honeywell International Inc. (Charlotte, NC, US)

1. A navigation system, comprising: a plurality of sensors onboard a vehicle, the sensors including at least one vision sensor; a data storage unit onboard the vehicle, the data storage unit including a map database comprising one or more terrain maps; and a processer onboard the vehicle, the processor operatively coupled to the sensors and the data storage unit, the processor including a position probability density function (PDF) filter, wherein the position PDF filter is operative to perform a method comprising: receiving image data from the at least one vision sensor, the image data corresponding to one or more terrain images, captured by the vision sensor, of a given area; receiving map data from the map database, the map data corresponding to a terrain map of the given area; generating a first PDF of one or more image features available in the image data; generating a second PDF of one or more map features available in the map data; generating a measurement vector PDF by a convolution of the first PDF and the second PDF; estimating a position vector PDF using a non-linear filter that receives the measurement vector PDF; and generating statistics from the estimated position vector PDF, the statistics including real-time measurement errors of position and angular orientation of the vehicle.

2. The navigation system of claim 1, wherein the non-linear filter comprises a Fokker-Planck filter.

3. The navigation system of claim 1, wherein the position PDF filter performs a correlation between the one or more image features in the image data and the one or more map features in the map data, to estimate position vector statistics of the vehicle relative to the terrain map.

4. The navigation system of claim 3, wherein the statistics include a mean vector and state covariance matrix of the correlation.

5. The navigation system of claim 1, wherein the at least one vision sensor comprises a camera mounted on the vehicle and operative to capture the one or more terrain images of the given area.

6. The navigation system of claim 1, wherein the sensors further include: an inertial measurement unit (IMU) operative to produce inertial measurements for the vehicle; and one or more aiding sensors.

7. The navigation system of claim 6, wherein the one or more aiding sensors comprise a Global Navigation Satellite System (GNSS) receiver, a barometer, or a magnetometer.

8. The navigation system of claim 6, further comprising: a processing unit operative to receive an output from the position PDF filter; and a navigation filter operative to receive an output from the processing unit; wherein the statistics of the estimated position vector PDF computed from the position PDF filter are extracted by the processing unit and sent to the navigation filter.

9. The navigation system of claim 8, further comprising: an inertial navigation system (INS) operatively coupled to the navigation filter; wherein the INS is operative to receive the inertial measurements from the IMU, and generate estimated kinematic state statistics for the vehicle.

10. The navigation system of claim 9, wherein the navigation filter comprises a Bayesian filter and includes: a prediction module operative to receive the estimated kinematic state statistics for the vehicle; and a correction module configured to receive an output from the prediction module, the correction module also configured to receive the extracted statistics from the processing unit and sensor measurement statistics from the one or more aiding sensors; wherein the navigation filter is operative to fuse the extracted statistics of the position vector PDF with the sensor measurement statistics, to estimate the position and angular orientation of the vehicle relative to the terrain map, and to aid in correction of errors in the estimated vehicle kinematic state statistics for the vehicle.

11. The navigation system of claim 10, wherein the correction module is configured to send an output signal to a subtractor, which is also configured to receive an output signal from the INS.

12. The navigation system of claim 11, wherein a differential signal between the output signals from the correction module and the INS is output from the subtractor as updated estimated vehicle kinematic state statistics, which are sent to other systems for further use in vehicle operation, and also fed back to the prediction module of the navigation filter.

13. The navigation system of claim 1, wherein the vehicle is an aircraft.

14. The navigation system of claim 1, wherein the vehicle is an unmanned aerial vehicle.

15. A method of navigating a vehicle with a map based, vision navigation system, the method comprising: capturing one or more terrain images of a given area over which the vehicle is traversing, the terrain images captured with at least one vision sensor onboard the vehicle; sending image data from the at least one vision sensor to a position probability density function (PDF) filter implemented in a processor onboard the vehicle, the image data corresponding to the one or more terrain images; sending map data from a map database onboard the vehicle to the position PDF filter, the map data corresponding to a terrain map of the given area; wherein the position PDF filter performs a method comprising: generating a first PDF of one or more image features available in the image data; generating a second PDF of one or more map features available in the map data; generating a measurement vector PDF by a convolution of the first PDF and the second PDF; estimating a position vector PDF using a non-linear filter that receives the measurement vector PDF; and generating statistics from the estimated position vector PDF, the statistics including real-time measurement errors of position and angular orientation of the vehicle; extracting the statistics from the position PDF filter; and sending the extracted statistics to a navigation filter onboard the vehicle.

16. The method of claim 15, wherein the non-linear filter comprises a Fokker-Planck filter.

17. The method of claim 15, wherein: the position PDF filter performs a correlation between the one or more image features in the image data and the one or more map features in the map data, to estimate position vector statistics of the vehicle relative to the terrain map; and the statistics include a mean vector and state covariance matrix of the correlation.

18. The method of claim 15, wherein the navigation filter fuses the extracted statistics with other sensor measurement statistics from other sensors onboard the vehicle, to estimate a position and angular orientation of the vehicle relative to the terrain map, and to aid in correction of errors in estimated vehicle kinematic state statistics for the vehicle.

19. The method of claim 18, further comprising: obtaining inertial measurements for the vehicle from an onboard inertial measurement unit (IMU); sending the inertial measurements to an onboard inertial navigation system (INS) operatively coupled to the navigation filter; wherein the INS is operative to generate the estimated kinematic state statistics for the vehicle.

20. The method of claim 19, wherein the navigation filter comprises a Bayesian filter and includes: a prediction module that receives the estimated kinematic state statistics for the vehicle; and a correction module that receives an output from the prediction module, the extracted statistics from the position PDF filter, and the other sensor measurement statistics from the other sensors.

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