Fast and accurate map merging for multi-robot systems

Authors:
Stefano Carpin
Affiliations:
School of Engineering, University of California, Merced, USA 95343
Venue:
Autonomous Robots
Year:
2008

Citing 6
Cited 7

Signals & systems (2nd ed.)

Signals & systems (2nd ed.)
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
Introduction to Robotics: Mechanics and Control

Introduction to Robotics: Mechanics and Control
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Topological Map Merging

International Journal of Robotics Research
Experiments with a Large Heterogeneous Mobile Robot Team: Exploration, Mapping, Deployment and Detection

International Journal of Robotics Research

A quantitative assessment of structural errors in grid maps

Autonomous Robots
Multi-robot team coordination through roles, positionings and coordinated procedures

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Distributed consensus algorithms for merging feature-based maps with limited communication

Robotics and Autonomous Systems
Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

Journal of Intelligent and Robotic Systems
Cooperative multi-robot map merging using Fast-SLAM

RoboCup 2009
Cooperative SLAM using M-Space representation of linear features

Robotics and Autonomous Systems
Anytime merging of appearance-based maps

Autonomous Robots

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Abstract

We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.