Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Object pose from 2-D to 3-D point and line correspondences
International Journal of Computer Vision
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Self-Localization in the RoboCup Environment
RoboCup-99: Robot Soccer World Cup III
Cooperative Probabilistic State Estimation for Vision-Based Autonomous Soccer Robots
RoboCup 2001: Robot Soccer World Cup V
Cooperative Probabilistic State Estimation for Vision-Based Autonomous Soccer Robots
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
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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 robots' 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 non-linearities 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.