LADAR-BASED MOTION ESTIMATION FOR NAVIGATION

• US 20080239279A1
• Filed: 03/28/2007
• Published: 10/02/2008
• Est. Priority Date: 03/28/2007
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Krishnaswamy, Kailash
• Granted Patent: US 7,463,340 B2
• Time in Patent Office: Days
• US Class Current: 356/5.01
• CPC Class Codes: G01C 21/1652 with ranging devices, e.g. LIDAR or RADAR
• CPC Class Codes: G01S 17/50 Systems of measurement based on relative movement of target
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 7/4802 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Abstract

A method for estimating motion with at least one laser radar is disclosed. The method involves scanning a first range image at a first time, locating a plurality of object features in the first range image, scanning a second range image at a second time, receiving motion data for a time period between the first and second times, and locating the plurality of object features in the second range image based, at least in part, on the motion data to determine an orientation of the object. Based on the orientation of the object, the method estimates motion by comparing the location of the plurality of object features in the first scanned range image to the location of the plurality of object features in the second scanned range image.

Claims

1. A method for estimating motion with at least one laser radar, the method comprising:

scanning a first range image at a first time;

identifying a plurality of object features in the first range image;

scanning a second range image at a second time;

receiving motion data from a host vehicle having the at least one laser radar for a time period between the first and second times;

identifying the plurality of object features in the second range image based, at least in part, on the motion data of the host vehicle to determine an orientation of least one object; and

based on the orientation of the at least one object with respect to a change in position of the host vehicle, estimating motion of the host vehicle by comparing the location of the plurality of object features in the first scanned range image to the location of the plurality of object features in the second scanned range image.

2. The method of claim 1, and further comprising estimating motion of the host vehicle based on a combination of the motion data from the host vehicle and the absolute orientation of the at least one object.

3. The method of claim 1, and further comprising:

receiving a global positioning signal; and

estimating motion based, at least in part, on the global positioning signal when the global positioning signal is available.

4. The method of claim 1, wherein identifying the plurality of object features in the second range image based, at least in part, on the motion data of the host vehicle includes:

locating the plurality of object features in the first range at a first scan location;

selecting a second scan location in the at least one second range based on the motion data and the first scan location; and

evaluating range scans near the second scan location to identify the actual location of the plurality of object features in the at least one second range.

5. The method of claim 4, wherein evaluating the range scans near the second scan location includes evaluating range scans in an area substantially surrounding the second scan location, wherein the size of the area is determined based on the approximate error in the motion data.

6. The method of claim 1, wherein estimating motion of the host vehicle comprises correlating the first and second scanned range images using an enhanced absolute orientation.

7. The method of claim 1, wherein estimating motion of the host vehicle by comparing the location of the plurality of object features comprises correlating 3-dimensional object features in the first range with 3-dimensional object features in the second range using at least one of a Kanade-Lucas-Tomasi corner detection algorithm and a Harris corner detection algorithm.

8. A motion estimation system, comprising:

at least one laser radar configured to scan a first range at a first time and a second range at a second time;

an inertial measurement unit configured to obtain motion data of a host vehicle for the time period between the first time and the second time; and

a processing unit coupled to the inertial measurement unit and the at least one laser radar, the processing unit configured to;

determine an enhanced absolute orientation of a plurality of 3-dimensional point features in at least one of the second ranges based at least in part on the motion data and a change in position of the host vehicle for the time period between the first time and the second time, and

estimate motion of the host vehicle by comparing the enhanced absolute orientation of the plurality of 3-dimensional point features in the two first ranges with the enhanced absolute orientation of the plurality of 3-dimensional point features in the respective two second ranges.

9. The system of claim 8, wherein the at least one laser radar is at least one of a range finder ladar, a flash ladar, and a Doppler ladar.

10. The system of claim 8, and further comprising a movement actuator coupled to the processing unit, the movement actuator operable to control motion based on control signals received from the processing unit.

11. The system of claim 8, and further comprising a display element coupled to the processing unit, the display element operable to display motion estimates based on signals received from the processing unit.

12. The system of claim 8, and further comprising a global positioning system sensor coupled to the processing unit, wherein the processing unit is configured to estimate motion based on data received from at least one of the global positioning system sensor, the inertial measurement unit, and at least one laser radar.

13. The system of claim 8, wherein the processing unit is configured to combine a motion estimate from the motion data and a position estimate from the at least one laser radar to obtain a composite motion estimate.

14. The system of claim 8, wherein the processing unit is further configured to:

identify a first scan location of the one or more 3-dimensional point features in each first range;

select a second scan location in the at least one second range based on the motion data from the inertial measurement unit; and

evaluate range scans near the second scan location to identify the actual scan location of the plurality of 3-dimensional point features in the at least one second range.

15. The system of claim 8, wherein the processing unit is configured to correlate the actual scan location of the plurality of 3-dimensional point features using at least one of a Kanade-Lucas-Tomasi corner detection algorithm and a Harris corner detection algorithm.

16. A program product comprising program instructions embodied on a processor-readable medium for execution by at least one programmable processor in a motion estimation system, wherein the program instructions are operable to cause the programmable processor to:

identify one or more 3-dimensional point features of an object in each of two first laser radar scans at a first time;

evaluate inertial measurement data of a host vehicle having the motion estimation system and navigating an area near the object for a time period between the first time and a second time;

identify the one or more 3-dimensional point features in at least one of two second laser radar scans received at the second time based at least in part on the inertial measurement data;

correlate the one or more 3-dimensional point features in the first laser radar scans to the one or more 3-dimensional point features in the second laser radar scans to determine an absolute orientation of the object; and

output the calculated motion estimate based, at least in part, on the absolute orientation of the object and a change in position of the host vehicle within the area being navigated.

17. The program product of claim 16, wherein the program instructions are further operable to cause the programmable processor to:

estimate the motion based on the inertial measurement data; and

combine the inertial measurement motion estimate with the motion estimate based on a comparison of the location of the one or more 3-dimensional point features in the first laser radar scans to the location of the one or more 3-dimensional point features in the second laser radar scans.

18. The program product of claim 16, wherein the program instructions that identify the one or more 3-dimensional point features in the at least one of two second laser radar scans received at the second time cause the programmable processor to:

identify a first scan location of the one or more 3-dimensional point features in each first laser radar scan;

select a second scan location in the at least one second laser radar scan based on the inertial measurement data and the first scan location; and

evaluate range scans near the second scan location to identify the actual location of the one or more 3-dimensional point features in the at least one second laser radar scan.

19. The program product of claim 18, wherein the program instructions that identify the one or more 3-dimensional point features in the at least one of two second laser radar scans received at the second time are further operable to cause the programmable processor to evaluate range scans in an area substantially surrounding the second scan location that identify the actual location of the one or more 3-dimensional point features in the at least one second laser radar scan, wherein the area is determined by an approximate error in the inertial measurement unit data.

20. The program product of claim 16, wherein the program instructions that correlate the one or more 3-dimensional point features in the first and second laser radar scans cause the programmable processor to use at least one of a Kanade-Lucas-Tomasi corner detection algorithm and a Harris corner detection algorithm to correlate the one or more 3-dimensional point features in an enhanced absolute orientation method.