Abstract-In this paper a ring of calibrated and synchronized cameras is used for achieving robot and obstacle localization inside a common observed area. To avoid complex appearance matching derived from the wide-baseline arrangement of cameras, a metric occupancy grid is obtained by intersection of silhouettes projected onto the floor. A particle filter is proposed for tracking multiple objects by using the grid as observation data. A clustering algorithm is included in the filter to increase the robustness and adaptability of the multimodal estimation task. To preserve identity of the robot from the set of tracked objects, odometry readings are used to compute a Maximum Likelihood (ML) global trajectory identification. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a human-cluttered scene.