International audience

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

This paper introduces an incremental method for "structure from motion" of complex scenes from a video sequence. More precisely, we estimate the 3D positions of the viewed points in images and the camera positions and orientations through the sequence. The method can be seen as a fast but accurate alternative to classical reconstruction methods that use bundle adjustment, and that can become slow and computation time expensive for very long scenes. Our results are compared to the reconstruction obtained by the classical hierarchical bundle adjustment method. They have also been successfully used as a reference sequence for the vision based localization of an autonomous mobile robot