SENSOR DATA PROCESSING

• US 20130058527A1
• Filed: 02/25/2011
• Published: 03/07/2013
• Est. Priority Date: 03/09/2010
• Status: Abandoned Application

• Current Assignee: University of Sydney
• Original Assignee: University of Sydney
• Inventors: Peynot, Thierry
• Application Number:
  US13/583,456
• Publication Number:
  US 20130058527A1
• Time in Patent Office: Days
• US Class Current: 382/103
• 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 17/89 for mapping or imaging
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/20164 Salient point detection; Corner detection
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30261 Obstacle
• CPC Class Codes: G06T 7/73 using feature-based methods

Abstract

A method and apparatus for processing sensor data comprising measuring a value of a first parameter of a scene using a first sensor (e.g. a camera) to produce a first image of the scene, measuring a value of a second parameter of the scene using a second sensor (e.g. a laser scanner) to produce a second image, identifying a first point of the first image that corresponds to a class of features of the scene, identifying a second point of the second image that corresponds to the class of features, projecting the second point onto the first image, determining a similarity value between the first point and the projection of the second point on to the first image, and comparing the determined similarity value to a predetermined threshold value. The method or apparatus may be used on an autonomous vehicle.

Claims

1. A method of processing sensor data, the method comprising:

measuring a value of a first parameter of a scene using a first sensor to produce a first image of the scene;

measuring a value of a second parameter of the scene using a second sensor to produce a second image of the scene;

identifying a first point, the first point being a point of the first image that corresponds to a class of features of the scene;

identifying a second point, the second point being a point of the second image that corresponds to the class of features;

projecting the second point onto the first image;

determining a similarity value between the first point and the projection of the second point on to the first image; and

comparing the determined similarity value to a predetermined threshold value.

2. A method according to claim 1, wherein the similarity value is a value related to a distance in the first image between the first point and the projection of the second point on to the first image.

3. A method according to claim 1, the method further comprising:

defining a neighbourhood in the second image around the second point; and

projecting the neighbourhood onto the first image;

wherein

the step of identifying the first point comprises identifying the first point such that the first point lies within the projection of the neighbourhood onto the first image.

4. A method according to claim 3, wherein the step of determining a value related to a distance comprises:

defining a probability distribution mask over the projection of the neighbourhood in the first image, the probability distribution mask being centred on the projection of the second point on the first image; and

determining a value of the probability distribution mask at the first point.

5. A method according to claim 1, wherein the first parameter is different to the second parameter.

6. A method according to claim 1, wherein the first sensor is a different type of sensor to the second sensor.

7. A method according to claim 6, wherein the first parameter is light intensity, the first sensor type is a camera, the second parameter is range, and the second sensor type is a laser scanner.

8. A method according to claim 1, the method further comprising calibrating the second image of the scene with respect to the first image of the scene.

9. A method according to claim 8, wherein the step of calibrating the second image of the scene with respect to the first image of the scene comprises determining a transformation to project points in the second image to corresponding points in the first image.

10. A method according to claim 9, wherein a step of projecting is performed using the determined transformation.

11. A method according to claim 1, wherein the similarity value is a value of a probability that the second image corresponds to the first image.

12. A method according to claim 3, wherein the similarity value is a value of a probability that the second image corresponds to the first image and where the probability is calculated using the following formula:

16. A computer program or plurality of computer programs arranged such that when executed by a computer system it/they cause the computer system to operate in accordance with the method of claim 1.

17. A machine readable storage medium storing a computer program or at least one of the plurality of computer programs according to claim 16.

13. Apparatus for processing sensor data, the apparatus comprising:

a first sensor for measuring a value of a first parameter of a scene to produce a first image of the scene;

a second sensor for measuring a value of a second parameter of the scene to produce a second image of the scene; and

one or more processors arranged to;

identify a first point, the first point being a point of the first image that corresponds to a class of features of the scene;

identify a second point, the second point being a point of the second image that corresponds to the class of features;

project the second point onto the first image;

determine a similarity value between the first point and the projection of the second point on to the first image; and

compare the determined similarity value to a predetermined threshold value.

14. An apparatus according to claim 13, wherein the similarity value is a value related to a distance in the first image between the first point and the projection of the second point on to the first image.

15. An autonomous vehicle comprising the apparatus of claim 13.