Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Robust mobile robot localization: from single-robot uncertainties to multi-robot interdependencies
Robust mobile robot localization: from single-robot uncertainties to multi-robot interdependencies
Cooperative multi-robot localization under communication constraints
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IEEE Transactions on Robotics
Cooperative AUV Navigation using a Single Maneuvering Surface Craft
International Journal of Robotics Research
Designing the HRTeam framework: lessons learned from a rough-and-ready human/multi-robot team
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Distributed vision-aided cooperative localization and navigation based on three-view geometry
Robotics and Autonomous Systems
Graph-based distributed cooperative navigation for a general multi-robot measurement model
International Journal of Robotics Research
Bearing-only Cooperative Localization
Journal of Intelligent and Robotic Systems
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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 distributed MAP estimator and comparing its accuracy to that of existing approaches.