Distributed Maximum A Posteriori Estimation for Multi-robot Cooperative Localization

Esha Nerurkar (1) , Stergios Roumeliotis (1) , Agostino Martinelli (2)

1 ECE - Department of Electrical and Computer Engineering [Minneapolis]
2 E-MOTION - Geometry and Probability for Motion and Action

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 memory requirements and computational complexity 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 singlepoint 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 simulations studies are presented for validating the performance of the distributed MAP estimator and comparing its accuracy to that of existing approaches.

Domaines: Robotique [cs.RO]

Esha Nerurkar, Stergios Roumeliotis, Agostino Martinelli. Distributed Maximum A Posteriori Estimation for Multi-robot Cooperative Localization. IEEE International Conference on Robotics and Automation, 2009. ICRA '09., May 2009, Kobe, Japan. pp.1402 - 1409. ⟨hal-00428663⟩