Means and Averaging in the Group of Rotations
SIAM Journal on Matrix Analysis and Applications
Decentralized Architecture for Asynchronous Sensors
Autonomous Robots
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
Fast and accurate map merging for multi-robot systems
Autonomous Robots
A Scalable Hybrid Multi-robot SLAM Method for Highly Detailed Maps
RoboCup 2007: Robot Soccer World Cup XI
Metric-topological maps from laser scans adjusted with incremental tree network optimizer
Robotics and Autonomous Systems
Covariance recovery from a square root information matrix for data association
Robotics and Autonomous Systems
Multi-robot visual SLAM using a Rao-Blackwellized particle filter
Robotics and Autonomous Systems
Multi-robot SLAM using ceiling vision
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Wide-baseline multiple-view correspondences
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
Robot-to-Robot Relative Pose Estimation From Range Measurements
IEEE Transactions on Robotics
Distributed Kalman filtering based on consensus strategies
IEEE Journal on Selected Areas in Communications
Information Sciences: an International Journal
Anytime merging of appearance-based maps
Autonomous Robots
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In this paper we present a solution for merging feature-based maps in a robotic network with limited communication. We consider a team of robots that explore an unknown environment and build local stochastic maps of the explored region. After the exploration has taken place, the robots communicate and build a global map of the environment. This problem has been traditionally addressed using centralized schemes or broadcasting methods. The contribution of this work is the design of a fully distributed approach which is implementable in scenarios with limited communication. Our solution does not rely on a particular communication topology and does not require any central agent, making the system robust to individual failures. Information is exchanged exclusively between neighboring robots in the communication graph. We provide distributed algorithms for solving the three main issues associated to a map merging scenario: establishing a common reference frame, solving the data association, and merging the maps. We also give worst-case performance bounds for computational complexity, memory usage, and communication load. Simulations and real experiments carried out using various vision sensors validate our results.