METHOD AND SYSTEM FOR GUIDING A VEHICLE WITH VISION-BASED ADJUSTMENT

• US 20080065287A1
• Filed: 05/31/2007
• Published: 03/13/2008
• Est. Priority Date: 01/04/2005
• Status: Active Grant

• Current Assignee: Deere & Company
• Original Assignee: Deere & Company
• Inventors: Reid, John, Pickett, Terence, Han, Shufeng
• Granted Patent: US 8,150,574 B2
• Time in Patent Office: Days
• US Class Current: 701/28
• CPC Class Codes: A01B 69/007 Steering or guiding of agricultural vehicles, e.g. steering of the tractor to keep the plough in the furrow
• CPC Class Codes: G01S 19/485 whereby the further system is an optical system or imaging system
• CPC Class Codes: G05D 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road

Abstract

Preliminary guidance data is determined for the vehicle during an evaluation time window. A vision module collects vision data from a vision module during the evaluation time window. Vision guidance data is determined from the collected vision data. A vision quality estimator estimates vision quality data for at least one of the vision data and the vision guidance data during the evaluation time window. The vision quality data is based on a regression path and density grid points. An adjuster adjusts the preliminary guidance data to a revised guidance data based on the vision guidance data such that the revised guidance data is registered with or generally coextensive with the vision guidance data, if the vision quality data exceeds a minimum threshold.

Claims

1. A method for guiding a vehicle, the method comprising:

determining preliminary guidance data for the vehicle based a location-determining receiver associated with the vehicle during an evaluation time window;

collecting vision data from a vision module associated with the vehicle during the evaluation time window;

determining vision guidance data from the collected vision data;

estimating vision quality data for the vision guidance data during the evaluation time window, the vision quality data based on a regression path and density grid points; and

adjusting the preliminary guidance data to a revised guidance data based on the vision guidance data such that the revised guidance data is registered with or generally coextensive with the vision guidance data data, if the vision quality data exceeds a minimum threshold.

2. The method according to claim 1 wherein the preliminary guidance data and revised guidance data each comprise at least one of heading data, off-track data, and curvature data.

3. The method according to claim 4 wherein the collecting vision data comprises collecting the vision data via a stereo vision imaging system.

4. The method according to claim 1 wherein the estimating of vision quality data comprises:

organizing a collected image into crop pixels and non-crop pixels;

defining a density grid based on the number of crop pixels within each grid cell;

defining a density grid points as those grid cells whose values are greater than a threshold;

arranging a regression path based on the density grid points, the regression path representing a possible position and possible orientation of a crop row or a windrow; and

determining a confidence index as vision quality data based on the defined density grid points and the regression path, the confidence index for application to a vehicle controller or an adjuster for steering a vehicle.

5. The method according to claim 4 wherein the confidence index comprises a product of the moment index and the cluster index.

6. The method according to claim 4 wherein the confidence index is determined in accordance with the following equation:

where C_M is the moment index given by

7. A method for estimating a vision quality data for guidance of a vehicle, the method comprising:

organizing a collected image into crop pixels and non-crop pixels;

defining a density grid based on the number of crop pixels within each grid cell;

defining a density grid points as those grid cells whose values are greater than a threshold;

arranging a regression path based on the density grid points, the regression path representing a possible position and possible orientation of a crop row or a windrow; and

determining a confidence index as vision quality data based on the defined density grid points and the regression path, the confidence index for application to a vehicle controller or an adjuster for steering a vehicle.

8. The method according to claim 7 wherein the determining of the confidence index comprises:

determining of a moment index of density grid points about the regression path; and

determining cluster index based on an inverse function of the standard deviation of the density grid points.

9. The method according to claim 7 wherein the confidence index comprises a product of the moment index and the cluster index.

10. The method according to claim 7 wherein the confidence index is determined in accordance with the following equation:

where C_M is the moment index given by

11. The method according to claim 7 further comprising:

collecting the image data via a stereo vision imaging system.

12. A system for guiding a vehicle, the system comprising:

a location module for determining a preliminary guidance data for the vehicle during an evaluation time window;

a vision module for collecting vision data associated with the vehicle during the evaluation time window;

an image processing system for determining vision guidance data from the collected vision data;

a vision quality estimator for estimating vision quality data for the vision guidance data during the evaluation time window, the vision quality data based on a regression path and density grid points; and

an adjuster for adjusting the preliminary guidance data to a revised guidance data based on the vision guidance data such that the revised guidance data is registered with or generally coextensive with the vision-derived location data, if the vision quality data exceeds a minimum threshold.

13. The system according to claim 12 wherein the vision quality estimator comprises:

an organizer for organizing a collected image into crop pixels and non-crop pixels;

a density grid definer for defining a density grid based on the number of crop pixels within each grid cell;

a filter for defining a density grid points as those grid cells whose values are greater than a threshold;

an arranger for arranging a regression path based on the density grid points, the regression path representing a possible position and possible orientation of a crop row or a windrow; and

an estimator for determining a confidence index as vision quality data based on the defined density grid points and the regression path, the confidence index for application to a vehicle controller or an adjuster for steering a vehicle.

14. The system according to claim 12 wherein the guidance data comprises at least one of heading data, off-track data, and curvature data.

15. A system for estimating a vision quality data for guidance of a vehicle, the system comprising:

an organizer for organizing a collected image into crop pixels and non-crop pixels;

a density grid definer for defining a density grid based on the number of crop pixels within each grid cell;

a filter for defining a density grid points as those grid cells whose values are greater than a threshold;

an arranger for arranging a regression path based on the density grid points, the regression path representing a possible position and possible orientation of a crop row or a windrow; and

an estimator for determining a confidence index as vision quality data based on the defined density grid points and the regression path, the confidence index for application to a vehicle controller or an adjuster for steering a vehicle.

16. The system according to claim 15 wherein the estimator is arranged to determine a moment index of density grid points about the regression path and to determine a cluster index based on an inverse function of the standard deviation of the density grid points.

17. The system according to claim 15 wherein the confidence index comprises a product of the moment index and the cluster index.

18. The system according to claim 15 wherein the confidence index is determined in accordance with the following equation:

where C_M is the moment index given by

19. The system according to claim 15 further comprising:

a stereo vision imaging system for collecting the image data.