METHOD FOR DETERMINING EGO-MOTION OF MOVING PLATFORM AND DETECTION SYSTEM

• US 20110074927A1
• Filed: 09/08/2010
• Published: 03/31/2011
• Est. Priority Date: 09/29/2009
• Status: Abandoned Application

• Current Assignee: National Tsing Hua University
• Original Assignee: National Tsing Hua University
• Inventors: Chen, Chih-Ting, Perng, Ming-Hwei
• Application Number:
  US12/877,447
• Publication Number:
  US 20110074927A1
• Time in Patent Office: Days
• US Class Current: 348/46
• CPC Class Codes: G06T 2207/10021 Stereoscopic video; Stereoscopic image sequence
• CPC Class Codes: G06T 2207/30244 Camera pose
• CPC Class Codes: G06T 7/285 using a sequence of stereo image pairs
• CPC Class Codes: G06T 7/579 from motion
• CPC Class Codes: G06T 7/593 from stereo images
• CPC Class Codes: H04N 13/239 using two 2D image sensors having a relative position equal to or related to the interocular distance H04N13/243 takes precedence

Abstract

A method for determining ego-motion of a moving platform and a system thereof are provided. The method includes: using a first lens to capture a first and a second left image at a first and a second time, and using a second lens to capture a first and a second right image; segmenting the images into first left image areas, first right image areas, second left image areas, and second right image areas; comparing the first left image areas and the first right image areas, the second left image areas and the second right image areas, and the first right image areas and the second right image areas, so as to find plural common areas; selecting N feature points in the common areas to calculate depth information at the first and the second time, and determining the ego-motion of the moving platform between the first time and the second time.

Claims

1. A method for determining ego-motion of a moving platform, comprising steps of:

(a) using a first lens to capture a first left image and a second left image at a first time and a second time respectively, and using a second lens to capture a first right image and a second right image at the first time and the second time respectively;

(b) segmenting the first left image into a plurality of first left image areas, segmenting the first right image into a plurality of first right image areas, segmenting the second left image into a plurality of second left image areas, and segmenting the second right image into a plurality of second right image areas, respectively by a processing module;

(c) comparing the first left image areas and the first right image areas, the second left image areas and the second right image areas, and the first right image areas and the second right image areas, respectively, so as to find a plurality of common areas corresponding to the first left image, the first right image, the second left image, and the second right image by a processing module;

(d) selecting N feature points in the common areas, wherein N is a positive integer by the processing module;

(e) using the N feature points to calculate a first depth information at the first time and a second depth information at the second time by the processing module; and

(f) determining the ego-motion of the moving platform between the first time and the second time according to the first depth information and the second depth information by the processing module.

2. The method according to claim 1, wherein in the step (b), the first left image, the first right image, the second left image, and the second right image are color segmented.

3. The method according to claim 1, wherein in the step (c), the first left image areas, the first right image areas, the second left image areas, and the second right image areas are compared in terms of global geometrical constraints, local geometrical characteristics, and color properties.

4. The method according to claim 3, wherein the global geometrical constraints comprise an epipolar constraint and an inter-area relative position constraint.

5. The method according to claim 3, wherein the local geometrical characteristics comprises edges, area, centroid, width, height, depth-to-width ratio, and convex hull.

6. The method according to claim 3, wherein the color properties comprise color gradient values of area edges and color statistics inside the areas.

7. The method according to claim 1, wherein in the step (d), the N feature points are selected at a fixed interval.

8. The method according to claim 1, wherein the depth information is distances between the N feature points and the first lens and between the N feature points and the second lens.

9. A detection system, comprising:

a moving platform;

a stereo camera including a first lens, disposed on the moving platform, for capturing a first left image and a second left image at a first time and a second time respectively, and a second lens, disposed on the moving platform, for capturing a first right image and a second right image at the first time and the second time respectively; and

a processing module, connected to the first lens and the second lens, for receiving the first left image, the second left image, the first right image, and the second right image, wherein the processing module segments the first left image into a plurality of first left image areas, segments the first right image into a plurality of first right image areas, segments the second left image into a plurality of second left image areas, and segments the second right image into a plurality of second right image areas, the processing module compares the first left image areas and the first right image areas, the second left image areas and the second right image areas, and the first right image areas and the second right image areas, so as to find a plurality of common areas corresponding to the first left image, the first right image, the second left image, and the second right image, the processing module selects N feature points in the common areas, N is a positive integer, the processing module uses the N feature points to calculate a first depth information at the first time and a second depth information at the second time, and the processing module determines the ego-motion of the moving platform between the first time and the second time according to the first depth information and the second depth information.

10. The detection system according to claim 9, wherein the processing module color segments the first left image, the first right image, the second left image, and the second right image.

11. The detection system according to claim 9, wherein the processing module compares the first left image areas, the first right image areas, the second left image areas, and the second right image areas in terms of global geometrical constraints, local geometrical characteristics, and color properties.

12. The detection system according to claim 11, wherein the global geometrical constraints comprise an epipolar constraint and an inter-area relative position constraint.

13. The detection system according to claim 11, wherein the local geometrical characteristics comprise edges, area, centroid, width, height, depth-to-width ratio, and convex hull.

14. The detection system according to claim 11, wherein the color properties comprise color gradient values of area edges and color statistics inside the areas.

15. The detection system according to claim 9, wherein the processing module selects the N feature points at a fixed interval.

16. The detection system according to claim 9, wherein the depth information is distances between the N feature points and the first lens and between the N feature points and the second lens.