Robert Hanek

IROS Conference 2002 Conference Paper

Fast image-based object localization in natural scenes

• Robert Hanek
• Thorsten Schmitt
• Sebastian Buck 0001
• Michael Beetz

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.

AAMAS Conference 2002 Conference Paper

The AGILO Autonomous Robot Soccer Team: Computational Principles, Experiences, and Perspectives

• Michael Beetz
• Sebastian Buck
• Robert Hanek
• Thorsten Schmitt

AAAI Conference 2002 Conference Paper

Watch Their Moves: Applying Probabilistic Multiple Object Tracking to Autonomous Robot Soccer

• Thorsten Schmitt
• Robert Hanek

In many autonomous robot applications robots must be capable of estimating the positions and motions of moving objects in their environments. In this paper, we apply probabilistic multiple object tracking to estimating the positions of opponent players in autonomous robot soccer. We extend an existing tracking algorithm to handle multiple mobile sensors with uncertain positions, discuss the specification of probabilistic models needed by the algorithm, and describe the required vision-interpretation algorithms. The multiple object tracking has been successfully applied throughout the RoboCup 2001 world championship.

IROS Conference 2001 Conference Paper

Cooperative probabilistic state estimation for vision-based autonomous mobile robots

• Thorsten Schmitt
• Robert Hanek
• Sebastian Buck 0001
• Michael Beetz

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.

IROS Conference 2000 Conference Paper

Vision-based localization and data fusion in a system of cooperating mobile robots

• Robert Hanek
• Thorsten Schmitt

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.