On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-robot collaboration for robust exploration
Annals of Mathematics and Artificial Intelligence
An Experimental Study of a Cooperative Positioning System
Autonomous Robots
Heterogeneous Teams of Modular Robots for Mapping and Exploration
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
International Journal of Robotics Research
A Theory of Network Localization
IEEE Transactions on Mobile Computing
Automatica (Journal of IFAC)
Optimal coverage for multiple hovering robots with downward facing cameras
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Mutual localization in a multi-robot system with anonymous relative position measures
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IEEE Transactions on Robotics
International Journal of Robotics Research
Interrobot transformations in 3-D
IEEE Transactions on Robotics
Planning and control for cooperative manipulation and transportation with aerial robots
International Journal of Robotics Research
Performance analysis of multirobot Cooperative localization
IEEE Transactions on Robotics
Robot-to-Robot Relative Pose Estimation From Range Measurements
IEEE Transactions on Robotics
On frame and orientation localization for relative sensing networks
Automatica (Journal of IFAC)
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We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e. measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.