Determining travel path features based on retroreflectivity

• US 8,699,755 B2
• Filed: 02/20/2009
• Issued: 04/15/2014
• Est. Priority Date: 02/20/2009
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: Navteq B.V.
• Inventors: Stroila, Matei Nicolai, Chen, Xin, Lu, Victor, Kohlmeyer, Bradley Dean, Moghaddam, Masha Kamali
• Primary Examiner(s): ENTEZARI, MICHELLE M
• Application Number:
  US12/389,553
• Publication Number:
  US 20100217529A1
• Time in Patent Office: 1,880 Days
• Field of Search: 382/104, 382106-107, 382/181, 702/5, 356/4.01, 342 53- 54, 342/70, 342/357.2, 342/357.52
• US Class Current: 382/106
• CPC Class Codes: G01C 3/06 Use of electric means to obtain final indication
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G09B 29/008 Touring maps or guides to public transport networks

Abstract

Systems, devices, features, and methods for determining geographic features corresponding to a travel path to develop a map database, such as a navigation database, are disclosed. For example, one method comprises emitting light from a light source, such as a LIDAR device, while on the travel path. Returning light is received based on the emitted light. The returning light is used to generate data points representing an area about the travel path. The data points are filtered as a function of a return intensity value to identify a feature associated with the travel path, in which the feature is treated with a retroreflective substance.

Claims

1. A method for determining geographic features corresponding to a road network to develop a navigation database, the method comprising:

collecting data points via a light detection and ranging (LIDAR) device while driving on a road of the road network;

determining whether energy of the collected data points is substantially constant through a scan of the LIDAR device;

filtering the collected data points based on a retroreflectivity threshold and whether the energy of the collected data points is substantially constant;

identifying data points that correspond to a road feature based on the filtered data points, wherein the road feature is treated with a retroreflective substance;

calculating a normal vector relative to a plane including the road feature that fits a portion of the identified data points; and

calculating an angle between the normal vector and a driving direction.

2. The method of claim 1, wherein the road feature comprises a road sign.

3. The method of claim 1, wherein identifying the data points comprises:

eliminating outlier data points based on a number of neighboring data points within a specific radius.

4. The method of claim 1, wherein identifying the data points comprises:

identifying a cluster of the filtered data points; and

applying a growing algorithm to identify additional data points corresponding to the road feature.

5. The method of claim 1, further comprising:

generating a data model representing the road feature based on the identified data points; and

storing the data model in the navigation database.

6. The method of claim 5, wherein generating the data model comprises:

fitting a geometric shape to a boundary associated with the identified data points.

7. The method of claim 5, wherein generating the data model comprises:

matching the identified data points with collected video or camera data.

8. The method of claim 1, wherein identifying the data points comprises:

applying a grid to a portion of the filtered data points to determine whether the portion of the filtered data points correspond to separate road features.

19. The method of claim 1, wherein the road feature comprises a lane marking.

20. The method of claim 1, wherein calculating the angle between the normal vector and the driving direction comprises calculating a dot product between the normal vector and the driving direction.

21. The method of claim 20, further comprising:

comparing the dot product to a first threshold; and

identifying the road feature as a road sign if the dot product exceeds the first threshold.

22. The method of claim 21, further comprising:

comparing the dot product to a second threshold; and

identifying the road feature as a lane marking if the dot product is less than the second threshold.

9. A method for determining features on a travel path to develop a map database, the method comprising:

collecting data points via a laser device while moving on the travel path;

extracting data points representing a path surface of the travel path from the collected data points;

determining whether energy of the data points is substantially constant through a scan of the laser device;

filtering the extracted data points based on a return intensity threshold and whether the energy of the data points is substantially constant, wherein values above the return intensity threshold indicate retroreflective substances;

calculating a normal vector relative to a plane of a road marking that fits a portion of the filtered data points;

calculating an angle between the normal vector and a driving direction; and

identifying data points that correspond to the road marking on the travel path based on the portion of the filtered data points.

10. The method of claim 9, wherein the marking comprises a road lane marking.

11. The method of claim 9, wherein extracting the data points representing the path surface comprises:

calculating an average ground elevation of the travel path.

12. The method of claim 11, further comprising:

determining borders of the travel path based on the average ground elevation.

13. The method of claim 9, further comprising:

generating a data model of the marking based on the identified data points; and

storing the data model in the map database.

14. A system for determining geographic features corresponding to a path to develop a navigation database, the system comprising:

a transportation device;

a light detection and ranging (LIDAR) device supported by the transportation device, the LIDAR device configured to collect data points corresponding to the path, wherein a path feature is identified by thresholding the collected data points as a function of a scan of the LIDAR device being substantially constant; and

a processor configured to identify a plane of data points among the collected data points and calculate an angle between a direction of the transportation device and a normal vector of the plane of data points of the path feature, wherein the angle indicates a type of the path feature.

15. The system of claim 14, wherein the transportation device comprises a vehicle.

16. The system of claim 14, wherein the transportation device comprises a device configured to be carried by a pedestrian.

17. The system of claim 14, wherein the path feature comprises a road sign.

18. The system of claim 14, wherein the path feature comprises a lane marking.