Distributed maximum a posteriori estimation for multi-robot cooperative localization

— This paper presents a distributed Maximum A Posteriori (MAP) estimator for multi-robot Cooperative Localization (CL). As opposed to centralized MAP-based CL, the proposed algorithm reduces the memory and processing requirements by distributing data and computations amongst the robots. Specifically, a distributed data-allocation scheme is presented that enables robots to simultaneously process and update their local data. Additionally, a distributed Conjugate Gradient algorithm is employed that reduces the cost of computing the MAP estimates, while utilizing all available resources in the team and increasing robustness to single-point failures. Finally, a computationally efficient distributed marginalization of past robot poses is introduced for limiting the size of the optimization problem. The communication and computational complexity of the proposed algorithm is described in detail, while extensive simulation studies are presented for validating the performance of the distribu...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Esha D. Nerurkar, Stergios I. Roumeliotis, Agostino Martinelli
• Keywords: Centralized Map-based Cl, Distributed Data-allocation Scheme, Efficient Distributed Marginalization, ICRA 2009, Robotics