Dynamic stereo with self-calibration

Tirumalai, Arun P.; Schunck, Brian G.; Jain, Ramesh

doi:10.1109/34.177383

Cited by 27 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1)The Calibration of the Cameras：Referred to the image square calibration [16], the eight-point algorithm [17], the automatic calibration [18], and approach in which the calibration is not needed [19], the image center is aligned with the paper ruler at the same height of the camera to make sure the cameras are placed at the same height, as shown in Figure 4. The photos in (a) and (b) represents the calibrated screen shot of cameras on the left and the right, respectively.…”

Section: B Camerasmentioning

confidence: 99%

Robot catching system with stereo vision and DSP platform

2011

View full text Add to dashboard Cite

The Texas Instrument DSP (TMS320DM642 EVM) is used as the implementation platform in our robot catcher system with two CCDs as source of the stereo vision. The system will separate the thrown-in target from the paired images and then calculate the centroid coordinates of each target image, thereby determining the space location of the object. The Lagrange interpolation formula and the linear function are used to simulate the ball trajectory and predict the catch position and then use the inverse kinematics to calculate the rotation angle and velocity of the 2-DOF robot arm system. After that, the control commands are sent by the RS-232 to the robot arm so as to catch the flying object at the expected location. This catcher robot system can catch the ball thrown to it from four meter away with an average success rate of 65%.

show abstract

Section: B Camerasmentioning

confidence: 99%

Robot catching system with stereo vision and DSP platform

2011

View full text Add to dashboard Cite

show abstract

“…We use a dynamic approach, as we apply a Kalman filter based formulation for recursive estimation of parallax maps by combining several measurements along the time line. The idea of integrating disparity measures using Kalman filter is far from new; early work [19], [10], [5], [13], [17] require motion and camera parameters to be known, because the depth (or its inverse) is the quantity that supports the aggregation from different frames. Most of them restricts to lateral motion.…”

Section: Introductionmentioning

confidence: 99%

Uncalibrated dynamic stereo using parallax

Malapelle

Fusiello

Rossi

et al. 2013

2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)

View full text Add to dashboard Cite

Abstract-In this paper a novel method for computing parallax maps from monocular and uncalibrated video sequences is described. Acquired frames are processed pairwise, starting from a first reference and progressively integrating information coming from subsequent frames in temporal order using a Kalman filter. In this way, temporal stabilization of the generated maps is obtained as well as more consistency with the real video content. Results and evidences coming from the benchmark of the system on both synthetic and natural images have also been reported, showing significant improvements with respect to the parallax maps obtained without temporal integration.

show abstract

“…By exploiting "active behaviors" it is possible to rely on self-calibration techniques which reduce the number of required parameters [23], [24]. Moreover, qualitative estimates of relative depth, like the time-to-impact, do not require the precise calibration of intrinsic parameters of the cameras.…”

Section: A I Calibrationmentioning

confidence: 99%

Active/dynamic stereo vision

Grosso

Tistarelli

1995

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

Abstract-Visual navigation is a challenging issue in automated robot control. In many robot applications, like object manipulation in hazardous environments or autonomous locomotion, it is necessary to automatically detect and avoid obstacles while planning a safe trajectory. In this context the detection of corridors of free space along the robot trajectory is a very important capability which requires nontrivial visual processing. In m t cases it is possible to take advantage of the active control of the cameras.In this paper we propose a cooperative schema in which motion and stereo vision are used to infer scene structure and determine free space areas. Binocular disparity, computed on several stereo images over time, is combined with optical flow from the same sequence to obtain a relative-depth map of the scene. Both the time-to-impact and depth scaled by the distance of the camera from the fixation point in space are considered as good, relative measurements which are based on the viewer, but centered on the environment.The need for calibrated parameters is considerably reduced by using an active control strategy. The cameras track a point in space independently of the robot motion and the full rotation of the head, which includes the unknown robot motion, is derived from binocular image data.The feasibility of the approach in real robotic applications is demonstrated by several experiments performed on real image data acquired from an autonomous vehicle and a prototype camera head.

show abstract

Dynamic stereo with self-calibration

Cited by 27 publications

References 15 publications

Robot catching system with stereo vision and DSP platform

Robot catching system with stereo vision and DSP platform

Uncalibrated dynamic stereo using parallax

Active/dynamic stereo vision

Contact Info

Product

Resources

About