A generic framework for scalable and convergent multi-robot active simultaneous localization, mapping and target tracking

In this paper, a new approach is proposed and analyzed for developing efficient and scalable methodologies for multi-robot active Cooperative Simultaneous Localization And Mapping and Target Tracking (C-SLAMTT). The proposed approach employs an active estimation scheme that switches among linear elements and, as a result, its computational requirements scale linearly with the number of estimated quantities (number of number of robots, landmarks and targets). The parameters of the proposed scheme are calculated off-line using a convex optimization algorithm which is based on Semi-Definite Programming (SDP) and approximation using Sum-of-Squares (SoS) polynomials. As shown by rigorous arguments, the estimation accuracy of the proposed scheme is equal to the optimal estimation accuracy plus a term that is inversely proportional to the number of estimator's switching elements (or, equivalently, to the memory storage capacity of the robots' equipment). The proposed approach can handle various types of constraints such as stay-within-an-area, obstacle avoidance and maximum speed constraints. The efficiency of the approach is demonstrated on a 3D active cooperative simultaneous mapping and target tracking application employing flying robots.