Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Multi-robot collaboration for robust exploration
Annals of Mathematics and Artificial Intelligence
An Experimental Study of a Cooperative Positioning System
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Exploring artificial intelligence in the new millennium
Robust mobile robot localization: from single-robot uncertainties to multi-robot interdependencies
Robust mobile robot localization: from single-robot uncertainties to multi-robot interdependencies
Predicting the Performance of Cooperative Simultaneous Localization and Mapping (C-SLAM)
International Journal of Robotics Research
Multi-robot exploration of an unknown environment, efficiently reducing the odometry error
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Consistent cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IEEE Transactions on Robotics
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Performance analysis of multirobot Cooperative localization
IEEE Transactions on Robotics
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We propose a new approach to the simultaneous cooperative localization of a very large group of simple robots capable of performing dead-reckoning and sensing the relative position of nearby robots. In the last decade, the use of distributed optimal Kalman filters (KF) to address this problem has been studied extensively. In this paper, we propose to use a very simple encounter based averaging process (denoted by EA). The idea behind EA is the following: every time two robots meet, they simply average their location estimates. We assume that two robots meet whenever they are close enough to allow relative location estimation and communication. At each meeting event, the robots average their location estimations thus reducing the localization error. Naturally, the frequency of the meetings affects the localization quality. The meetings are determined by the robots' movement pattern. In this work we consider movement patterns which are ''well mixing'', i.e. every robot meets other robots and eventually all of the robots frequently. For such a movement pattern, the time course of the expected localization error is derived. We prove that EA is asymptotically optimal and requires significantly less computation and communication resources than KF.