This paper describes a new vision based method for the Simultaneous Localization and Mapping of mobile robots. The only data used is a video input from a moving calibrated monocular camera. From the detection and matching of interest points in images at video rate, robust estimates of the camera poses are computed in real-time and a 3D map of the environment is reconstructed. The computed 3D structure is constantly refined thanks to the introduction of a fast and local bundle adjustment method that makes this approach particularly accurate and reliable. Actually, this method can be seen as a new visual tool that may be used in conjunction with usual systems (GPS, inertia sensors, etc) in SLAM applications.