An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras

Due to their wide field of view, omnidirectional cameras are becoming ubiquitous in many mobile robotic applications.  A challenging problem consists of using these sensors, mounted on mobile robotic platforms, as visual compasses (VCs) to provide an estimate of the rotational motion of the camera/robot from the omnidirectional video stream. Existing VC algorithms suffer from some practical limitations, since they require a precise knowledge either of the camera-calibration parameters, or the 3-D geometry of the observed scene. In this paper we present a novel multiple-view geometry constraint for paracatadioptric views of lines in 3-D, that we use to design a VC algorithm that does not require either the knowledge of the camera calibration parameters, or the 3-D scene geometry. In addition, our algorithm runs in real time since it relies on a closed-form estimate of the camera/robot rotation, and can address the image-feature correspondence problem. Extensive simulations and experiments with real robots have been performed to show the accuracy and robustness of the proposed method.

► We designed an algorithm to estimate the rotational motion of a robot from onmidirectional images. ► Our visual-compass algorithm does not require knowledge of the 3-D scene and of camera calibration. ► Our algorithm relies upon a new theoretical result that relates 3-D lines observed by the omnidirectional camera. ► Extensive simulation and experimental results show the accuracy and robustness of our algorithm.