Automatic Self-calibration of a Vision System during Robot Motion

— This paper presents a new technique to estimate the extrinsic parameters of a robot-vision sensor system. More in general, this technique can be adopted to calibrate any robot bearing sensor. It is based on the Extended Kalman Filter. It is very simple and allows an automatic self-calibration during the robot motion. It only requires a source of light in the environment and an odometry system on the robot. The strategy is theoretically validated through an observability analysis which takes into account the system nonlinearities. This analysis shows that the system contains all the necessary information to perform the self-calibration. Furthermore, many accurate simulations and experiments performed on a real platform equipped with encoder sensors and an omnidirectional conic vision sensor, show the exceptional performance of the strategy. Key Words: Camera Self-Calibration, Non-linear Observability, Robot Navigation, Extended Kalman Filter

• Type: Conference
• Year: 2006
• Where: ICRA
• Authors: Agostino Martinelli, Davide Scaramuzza, Roland Siegwart
• Keywords: Extended Kalman Filter, ICRA 2006, Robot Bearing Sensor, Robot-vision Sensor System, Robotics