In many robot applications, autonomous robots must be capable of localizing the objects they are to manipulate. In this paper we address the object localization problem by fitting a parametric curve model to the object contour in the image. The initial prior of the object pose is iteratively refined to the posterior distribution by optimizing the separation of the object and background. The local separation criteria are based on local statistics which are iteratively computed from the object and background region. No prior knowledge on color distributions is needed. Experiments show that the method is capable of localizing objects in a cluttered and textured scene even under strong variations of illumination. The method is able to localize a soccer ball within frame rate.

With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this paper, we develop and analyze a probabilistic, vision-based state estimation method for individual autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.

The article addresses the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters, an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge, local statistics are learned for both sides of the edge. These local statistics provide locally adapted criteria to distinguish the two sides of the edge, even in the presence of spatially changing properties such as texture, shading, or color. Based on the local statistics, the model parameters are iteratively refined using a MAP estimation. Experiments with RGB images show that the method is capable of achieving high subpixel accuracy and robustness even in the presence of texture, shading, clutter, and partial occlusion.

With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. We develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.

The approach presented in this paper allows a team of mobile robots to estimate cooperatively their poses, i.e. positions and orientations, and the poses of other observed objects from images. The images are obtained by calibrated color cameras mounted on the robots. Model knowledge of the robot environment, the geometry of observed objects, and the characteristics of the cameras are represented in curve functions which describe the relation between model curves in the image and the sought pose parameters. The pose parameters are estimated by minimizing the distance between model curves and actual image curves. Observations from possibly different view points obtained at different times are fused by a method similar to the extended Kalman filter. In contrast to the extended Kalman filter, which is based on a linear approximation of the measurement equations, we use an iterative optimization technique which takes nonlinearities into account. The approach has been successfully used in robot soccer, where it reliably maintained a joint pose estimate for the players and the ball.

In this work, we use points, fines, and the linear extremal contours of cylinders to estimate the position and orientation of the camera in the world coordinate system. Other line-based pose estimation methods use the correspondences between 3D lines in space and 2D image lines, although the model and its observation are finite line segments. We present a noise model describing the probabilistic relationship between 3D lines and cylinders and their noisy observations. The noise model takes the finite nature of the observation into account. Position and orientation of cameras are estimated using a maximum-likelihood approach. Covariance matrices, confidence limits, and standard translation and rotation errors are estimated by singular value decomposition of the Jacobian matrix. The method provides clear indications on the reliability of each of the estimated parameters, and enables the user to add appropriate information, in terms of feature correspondence, to improve the accuracy if necessary. Simulation results are used to compare this method with some of the previously published ones. The algorithm is currently being used on real data for the update of 3D CAD models of industrial environments.