Method of estimating a motion of a multiple camera system, a multiple camera system and a computer program product

• US 20110316980A1
• Filed: 12/21/2009
• Published: 12/29/2011
• Est. Priority Date: 12/22/2008
• Status: Abandoned Application

• Current Assignee: Nederlandse Organisatie Voor Toegepast-Natuurwetenschappelijk Onderzoek Tno
• Original Assignee: Nederlandse Organisatie Voor Toegepast-Natuurwetenschappelijk Onderzoek Tno
• Inventors: van der Mark, Wannes, Dubbelman, Gijs
• Application Number:
  US13/141,312
• Publication Number:
  US 20110316980A1
• Time in Patent Office: Days
• US Class Current: 348/47
• CPC Class Codes: G06T 7/285 using a sequence of stereo image pairs
• CPC Class Codes: G06T 7/97 Determining parameters from multiple pictures depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85
• CPC Class Codes: G06V 10/757 Matching configurations of points or features

Abstract

The invention relates to a method of correcting a bias in a motion estimation of a multiple camera system in a three-dimensional (3D) space, wherein the fields of view of multiple cameras at least partially coincide. The method comprises the step of computing a first and second set of distribution parameters associated with corresponding determined 3D positions of image features in subsequent image sets. Further, the method comprises the step of estimating a set of motion parameters representing a motion of the multiple camera system. The method also comprises the steps of improving the computed first or second set of distribution parameters and improving the estimated set of motion parameters. Further, the method comprises calculating a bias direction based on the initially estimated set of motion parameters and on the improved estimated set of motion parameters.

Claims

1. A method of correcting a bias in a motion estimation of a multiple camera system in a three-dimensional (3D) space, wherein the fields of view of multiple cameras at least partially coincide, the method comprising the steps of:

providing a subsequent series of image sets that have substantially simultaneously been captured by the multiple camera system;

identifying a multiple number of corresponding image features in a particular image set;

determining 3D positions associated with said image features based on a disparity in the images in the particular set;

determining 3D positions associated with said image features in a subsequent image set;

computing a first and second set of distribution parameters, including covariance parameters, associated with corresponding determined 3D positions, the computing step including error propagation;

estimating an initial set of motion parameters representing a motion of the multiple camera system between the time instant associated with the particular image set and the time instant of the subsequent image set, based on 3D position differences of image features in images of the particular set and the subsequent set;

correcting the determined 3D positions associated with the image features in the image sets, using the initial set of motion parameters;

correcting the computed first and second set of distribution parameters by error propagation of the distribution parameters associated with the corresponding corrected 3D positions;

improving the estimated set of motion parameters using the corrected computation of the set of distribution parameters;

calculating a bias direction based on the initial set of motion parameters and the improved set of motion parameters;

calculating a bias correction motion by inverting and scaling the bias direction; and

correcting the initial set of motion parameters by combining the initial set of motion parameters with the bias correction motion.

2. A method according to claim 1, wherein the step of estimating a set of motion parameters is also based on the computed first and second set of distribution parameters.

3. A method according to claim 1, wherein the step of improving the computed first or second set of distribution parameters comprises the substeps of:

mapping corresponding positions of image features in images of the particular set and the subsequent set;

constructing improved 3D positions of the mapped image features;

remapping the constructed improved 3D positions; and

determining improved covariance parameters.

4. A method according to claim 1, further comprising the step of estimating an absolute bias correction, including multiplying the calculated bias direction by bias gain factors.

5. A method according to claim 1, wherein the motion parameters include 3D motion information and 3D rotation information of the multiple camera system.

6. A method according to claim 1, wherein the image features are inliers.

7. A multiple camera system for movement in a three-dimensional (3D) space, comprising a multiple number of cameras having fields of view that at least partially coincide, the cameras being arranged for subsequently substantially simultaneously capturing image sets, the multiple camera system further comprising a computer system provided with a processor that is arranged for performing the steps of:

providing a subsequent series of image sets that have substantially simultaneously been captured by the multiple camera system;

identifying a multiple number of corresponding image features in a particular image set;

determining 3D positions associated with said image features based on a disparity in the images in the particular set;

determining 3D positions associated with said image features in a subsequent image set;

computing a first and second set of distribution parameters, including covariance parameters, associated with corresponding determined 3D positions, the computing step including error propagation;

estimating an initial set of motion parameters representing a motion of the multiple camera system between the time instant associated with the particular image set and the time instant of the subsequent image set, based on 3D position differences of image features in images of the particular set and the subsequent set;

correcting the determined 3D positions associated with the image features in the image sets, using the initial set of motion parameters;

correcting the computed first and second set of distribution parameters by error propagation of the distribution parameters associated with the corresponding corrected 3D positions;

improving the estimated set of motion parameters using the corrected computation of the set of distribution parameters;

calculating a bias direction based on the initial set of motion parameters and the improved set of motion parameters;

calculating a bias correction motion by inverting and scaling the bias direction; and

correcting the initial set of motion parameters by combining the initial set of motion parameters with the bias correction motion.

8. A computer program product for estimating a motion of a multiple camera system in a three-dimensional (3D) space, wherein the fields of view of multiple cameras at least partially coincide, the computer program product comprising computer readable code for causing a processor to perform the steps of:

providing a subsequent series of image sets that have substantially simultaneously been captured by the multiple camera system;

identifying a multiple number of corresponding image features in a particular image set;

determining 3D positions associated with said image features based on a disparity in the images in the particular set;

determining 3D positions associated with said image features in a subsequent image set;

computing a first and second set of distribution parameters, including covariance parameters, associated with corresponding determined 3D positions, the computing step including error propagation;

estimating an initial set of motion parameters representing a motion of the multiple camera system between the time instant associated with the particular image set and the time instant of the subsequent image set, based on 3D position differences of image features in images of the particular set and the subsequent set;

correcting the determined 3D positions associated with the image features in the image sets, using the initial set of motion parameters;

correcting the computed first and second set of distribution parameters by error propagation of the distribution parameters associated with the corresponding corrected 3D positions;

improving the estimated set of motion parameters using the corrected computation of the set of distribution parameters;

calculating a bias direction based on the initial set of motion parameters and the improved set of motion parameters;

calculating a bias correction motion by inverting and scaling the bias direction; and

correcting the initial set of motion parameters by combining the initial set of motion parameters with the bias correction motion.