This work addresses the SLAM problem for stereo vision systems under the unified formulation of particle filter methods. In contrast to most existing approaches to visual SLAM, the present method does not rely on restrictive smooth camera motion models, but on computing incremental 6D pose differences from the image flow through a probabilistic visual odometry method. Moreover, our observation model, which considers both the 3D positions and the SIFT descriptors of the landmarks, avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all the possible associations. We have experimentally validated our research with two experiments in indoor scenarios. Key words: computer vision, stereo vision, SLAM, robot localization, particle filters

This paper describes an approach to solve the Simultaneous Localization and Mapping (SLAM) problem with a team of cooperative autonomous vehicles. We consider that each robot is equipped with a stereo camera and is able to observe visual landmarks in the environment. The SLAM approach presented here is feature-based, thus the map is represented by a set of three dimensional landmarks each one defined by a global position in space and a visual descriptor. The robots move independently along different trajectories and make relative measurements to landmarks in the environment in order to jointly build a common map using a Rao-Blackwellized particle filter. We show results obtained in a simulated environment that validate the SLAM approach. The process of observing a visual landmark is simulated in the following way: first, the relative measurement obtained by the robot is corrupted with gaussian noise, using a noise model for a standard stereo camera. Second, the visual description of the landmark is altered by noise, simulating the changes in the descriptor which may occur when the robot observes the same landmark under different scales and viewpoints. In addition, the noise in the odometry of the robots also takes values obtained from real robots. We propose an approach to manage data associations in the context of visual features. Different experiments have been performed, with variations in the path followed by the robots and the parameters in the particle filter. Finally, the results obtained in simulation demonstrate that the approach is suitable for small robot teams.

Abstract: When having a multi-robot system in which each robot contructs its own local map, it can be necessary to perform the fusion of these local maps into a global one. The Map Fusion problem involves the consecution of two different tasks: Map Align-ment and Map Merging. The Map Alignment consists in computing the transformation, if existent, between the local maps. In this way, all the observations will be referenced to a common global frame. In the Map Merging stage, a global map is constructed from the local maps by integrating their information. This pa-per is focussed on the first step: Map Alignment. Par-ticularly, a collection of aligning algorithms is eval-uated in order to select the method that obtains the best results in terms of accuracy and stability. The experiments are performed in a multi-robot system, in which each robot constructs its own local map in-dependently. These maps are visual landmark-based and the mapping algorithm used is FastSLAM. Key-Words: multi-robot system, map alignment, vi-sual SLAM. 1