Robot navigation without calibration

Robots that never need any calibration of their kinematics, their actuators or their sensors promise great advantages in terms of maintenance cost, robustness and adaptability. Here we propose an approach for calibration-free vision based navigation in networks of corridors. The underlying concepts are based on the utilization of laws of projective geometry that always apply, regardless of camera characteristics, and in some cases on learning by doing. Our approach is based on a vehicle-mounted camera looking into an approximately forward direction as the main sensor and a qualitative knowledge of the appearance of objects of interest, such as floors and walls of corridors. The skills developed on the basis of the approach were intensively tested and have proved their effectiveness, adaptability and robustness under real-world conditions in two different office environments and with two robots, a humanoid personal robotic assistant, HERMES and a transportation robot, ATHENE II.