2013
DOI: 10.2478/mms-2013-0020
|View full text |Cite
|
Sign up to set email alerts
|

Automatic Calibration of Stereoscopic Cameras in an Electronic Travel Aid for the Blind

Abstract: The article describes a technique developed for identification of extrinsic parameters of a stereovision camera system for the purpose of image rectification without the use of reference calibration objects. The goal of the presented algorithm is the determination of the mutual position of cameras, under the assumption that they can be modeled by pinhole cameras, are separated by a fixed distance and are moving through a stationary scene. The developed method was verified experimentally on image sequences of a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2014
2014
2021
2021

Publication Types

Select...
1
1
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 7 publications
0
3
0
Order By: Relevance
“…Among them, 17 works conducted both simulation and field tests. In simulations, unit tests are most popular to evaluate subsystems, such as mixed verification of camera calibration [78], the capacity of proposed novel ultrasonic signals processing [79], etc. In experiments, the results of the unit test (122) and the integrated test (97) are both numerous.…”
Section: Discussionmentioning
confidence: 99%
“…Among them, 17 works conducted both simulation and field tests. In simulations, unit tests are most popular to evaluate subsystems, such as mixed verification of camera calibration [78], the capacity of proposed novel ultrasonic signals processing [79], etc. In experiments, the results of the unit test (122) and the integrated test (97) are both numerous.…”
Section: Discussionmentioning
confidence: 99%
“…We use this distance in our filter's observation function as an implicit constraint that guarantees the correct estimation of the joint offsets. To accomplish this, we first obtain the transformations R T L Θ k and L T R Θ k , using equations (1) and (8). We rewrite these two transformations as in (12) and apply equations (13) to (15) to each pair of features i represented in Z k F L and Z k F R .…”
Section: B Observation Modelmentioning
confidence: 99%
“…In [8] a system to estimate the extrinsic parameters between two cameras without any reference objects or human intervention, by integrating visual information with angular velocities given by a gyroscope to reject image features that are useless to the estimation, is presented. The system described in [3] uses a Kalman Filter to continuously estimate the extrinsic parameters of the stereo cameras and the one explained in [6] uses recursive filtering techniques and plane induced homographies between successive frames to optimize the calibration parameters between the cameras, in an online manner.…”
Section: Introductionmentioning
confidence: 99%