H. Araujo

Iterative multistep explicit camera calibration

Perspective camera calibration has been a research subject for a large group of researchers. However, only a small number of those methods base their approaches on the use of monoplane calibration points. We developed one of those methodologies using monoplane calibration points to perform an explicit three-dimensional (3-D) camera calibration. This methodology is based on an iterative approach. To avoid the singularities obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multistep procedure and requires a first-guess solution for the intrinsic parameters. These intrinsic parameters are updated and their accuracy increased during the iterative procedure. All computations required are linear and in addition to the extrinsic parameters the proposed method also computes the first coefficient of the radial distortion (k/sub 1/) and the skew angle. A first-guess value for the focal length of the lens is required but its value is iteratively updated using the Gauss lens model. This methodology also includes the uncertainty horizontal image scale factor (S/sub x/) on the set of calibration parameters to be computed, which makes this approach independent of the accuracy of the horizontal scale factor. The proposed methodology has the advantage that it can be used with monoplane calibration data with no restrictions for the pose geometry of the camera. In this approach the pose estimation problem is treated separately, computing the pose orientation of the camera in a first step and using this information to compute the pose location. A model for the expected stability of the camera look angles from noisy image data and its stability analysis as a function of the pose geometry of the camera is presented. Several experimental and simulated analyzes were performed and are presented.

Camera recalibration with hand-eye robotic system

The authors address the geometric modeling aspects related to the use of a pair of video cameras mounted on a six-degree-of-freedom manipulator in a hand-eye configuration. The information obtained by these sensors will be used to generate the depth map of the environment where the manipulator works. The procedure for determining the image formation parameters is known by camera calibration, and a method to optimize these calibration parameters is presented. A calibration matrix invariant to movement is developed. The optimization process is applied to this matrix to obtain better performance in the numerical data. This optimization process is the basis for a recalibration procedure that exploits the capability of positioning of the cameras in different positions within the workspace of the robot. The first optimization process used is a recursive form of the least-squares method and the second is the Kalman filter. Experimental results obtained show that the recalibration technique gives some improvement in the numerical data and stabilizes the calibration matrix along the different positions of the vision system within the robot workspace.<<ETX>>

Control issues to improve visual control of motion: applications in active tracking of moving targets

This paper deals with active tracking of 3D moving targets. Tracking is based on different visual behaviors, namely smooth pursuit and vergence control. The performance and robustness in visual control of motion depends both on the vision algorithms and the control structure. In this work we evaluate these two aspects, characterize the delays, and discuss ways to cope with latency while improving system performance. Kalman filtering is used to achieve smooth behaviors and increase visual processing robustness. A specific Kalman filter structure is proposed and its tuning and initialization are discussed. Delays and system latencies substantially affect the performance of visually guided systems. Interpolation is used to cope with visual processing delays. Model predictive control strategies are proposed to compensate for the mechanical latency in visual control of motion.

Parallel texture analysis using high-order statistics and neural networks

In this paper we present a texture analysis and classification method based on the computation of high-order statistics of gray-levels or selected features. The computation of these high-order statistics can be performed in parallel in non-overlapping windows that cover the full image. Classification is performed by neural networks. In this extended summary results are presented for backpropagation networks only, but several other types of neural nets were also tested.<<ETX>>

Depth perception using focus with hand-eye robotic system

The authors explore focus to obtain depth or structure perception of the world. They propose to vary the degree of focusing by moving the camera with respect to the object position. In this case the camera is attached to the tool of a manipulator in a hand-eye configuration with the position of the camera always known. This approach ensures that the focused areas of the images are always subjected to the same magnification. To measure the focus quality, operators are used to quantify the high-frequency content of the image. Different types of operators were tested and the results compared.<<ETX>>

Depth recovery using active focus in robotics (vision)

In practice focusing can be obtained by displacing the sensor plate with respect to the image plane, by moving the lens or by moving the object with respect to the optical system. Moving the lens or sensor plate with respect to each other, causes changes of the magnification and corresponding changes on the object coordinates. In order to overcome these problems, the authors propose a technique to vary the degree of focusing by moving the camera with respect to the object position. The camera is attached to the tool of a manipulator in a hand-eye configuration with the position of the camera always known. This approach ensures that the focused areas of the image are always subjected to the same magnification. To measure the focus quality operators are used to evaluate the quantity of high-frequency components on the image. Different types of these operators was tested and the results compared.<<ETX>>

Improving camera calibration by using multiple frames in hand-eye robotic systems

Addresses the geometric modelling aspects related with the use of a pair of video cameras mounted on a six degrees-of-freedom manipulator in an hand-eye configuration. The information obtained by these sensors shall be used to generate the depth map of the environment where the manipulator works. The procedure to determining the image formation parameters is known by camera calibration and a method to optimize these calibration parameters is presented. After presenting the calibration technique used, the authors develop a calibration matrix invariant to movement. The optimization process is applied to this matrix to obtain a better performance in the numerical data.<<ETX>>

A Multi-Sensor Distributed System for a Flexible Assembly Cell

The use of multi-sensor based flexible assembly cells can increase substantially the scope of applications of manipulators in industry. This paper is concerned with the development of a general purpose assembly cell which can be used to evaluate the feasibility of automatic assembly of different products. The cell is built around a manipulator with six degrees of freedom, a conveyor belt and an assembly table. The sensorial system of the cell comprises real-time vision, gripper force and various detectors on the conveyor belt and assembly table. A simple and high performance distributed control structure enables the integration of redundant sensorial information and real-time path control.