Fast and accurate map merging for multi-robot systems

When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

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Tomographic Feature-Based Map Merging for Multi-Robot Systems

Electronics ◽

10.3390/electronics9010107 ◽

2020 ◽

Vol 9 (1) ◽

pp. 107 ◽

Cited By ~ 2

Author(s):

Heoncheol Lee

Keyword(s):

Gaussian Mixture Models ◽

Search Space ◽

Gaussian Mixture ◽

Transformation Matrix ◽

Robot Systems ◽

Feature Based ◽

The Individual ◽

Merging Method ◽

Multi Robot ◽

Map Merging

Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.

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Map Merging for Multi-robot Systems Based on Hierarchical Topology Structure under RTM Framework

ROBOT ◽

10.3724/sp.j.1218.2013.00292 ◽

2013 ◽

Vol 35 (3) ◽

pp. 292

Author(s):

Songmin JIA ◽

Yuchen LI ◽

Ke WANG ◽

Xiuzhi LI ◽

Bing GUO

Keyword(s):

Topology Structure ◽

Hierarchical Topology ◽

Robot Systems ◽

Multi Robot ◽

Map Merging

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Improved Feature Map Merging Using Virtual Supporting Lines for Multi-Robot Systems

Advanced Robotics ◽

10.1163/016918611x584631 ◽

2011 ◽

Vol 25 (13-14) ◽

pp. 1675-1696 ◽

Cited By ~ 12

Author(s):

Heon-Cheol Lee ◽

Beom-Hee Lee

Keyword(s):

Feature Map ◽

Robot Systems ◽

Multi Robot ◽

Map Merging

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Selective spectral correlation for efficient map merging in multi‐robot systems

Electronics Letters ◽

10.1049/ell2.12139 ◽

2021 ◽

Author(s):

Heoncheol Lee

Keyword(s):

Spectral Correlation ◽

Robot Systems ◽

Multi Robot ◽

Map Merging

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Multi‐hypothesis map merging with sinogram‐based PSO for multi‐robot systems

Electronics Letters ◽

10.1049/el.2016.1041 ◽

2016 ◽

Vol 52 (14) ◽

pp. 1213-1214 ◽

Cited By ~ 11

Author(s):

H.C. Lee ◽

B.S. Roh ◽

B.H. Lee

Keyword(s):

Robot Systems ◽

Multi Robot ◽

Map Merging

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Grid Map Merging with Ant Colony Optimization for Multi-Robot Systems

10.5772/intechopen.98223 ◽

2021 ◽

Author(s):

Heoncheol Lee

Keyword(s):

Ant Colony Optimization ◽

Ant Colony ◽

Working Environment ◽

Grid Map ◽

Localization And Mapping ◽

Robot Systems ◽

The Individual ◽

Merging Algorithms ◽

Multi Robot ◽

Map Merging

Multi-robot systems have recently been in the spotlight in terms of efficiency in performing tasks. However, if there is no map in the working environment, each robot must perform SLAM which simultaneously performs localization and mapping the surrounding environments. To operate the multi-robot systems efficiently, the individual maps should be accurately merged into a collective map. If the initial correspondences among the robots are unknown or uncertain, the map merging task becomes challenging. This chapter presents a new approach to accurately conducting grid map merging with the Ant Colony Optimization (ACO) which is one of the well-known sampling-based optimization algorithms. The presented method was tested with one of the existing grid map merging algorithms and showed that the accuracy of grid map merging was improved by the ACO.

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