The fully flexible navigation of autonomous vehicles in industrial environments is still unsolved. It is hard to conciliate strict precision requirements with quick adaptivity to new settings without undergoing costly rearrangements. We are pursuing a research project trying to combine the precision of laser-based local positioning with the flexibility of vision-based robot motion estimation. An enhanced circle approach to dynamic triangulation combining laser and odometric signals has been used to improve positioning accuracy. As regards to vision, a novel technique relating the deformation of contours in an image sequence to the 3D motion underwent by the camera has been developed. Interestingly, contours are fitted to objects already present in the environment, without requiring any presetting. In this paper, we describe a practical experience conducted in the warehouse of a beer production factory in Barcelona. A database containing the laser readings, image sequences and robot odometry along several trajectories was compiled, and subsequently processed off-line in order to assess the accuracies of both techniques under a variety of circumstances. In all, vision-based estimation turned out to be about one order of magnitude less precise than laser-based positioning, which qualifies the vision-based technique as a promising alternative to accomplish robot transfers across long distances, such as those needed in a warehouse, while backing up on laser-based positioning when accurate docking for loading and unloading operations is needed.

This paper shows how to adapt the set inversion via interval analysis algorithm (SIVIA) to the problem of mobile robot start-up positioning with a goniometric sensor. This approach is an alternative way to Markov, Monte Carlo and neural networks techniques, among others, that deal with that problem. SIVIA brings a global solution and confines the desired result into a union of intervals.