scholarly journals Map-merging in Multi-robot Simultaneous Localization and Mapping Process Using Two Heterogeneous Ground Robots

2019 ◽  
Vol 32 (4) ◽  
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Localization And Mapping ◽  
Mapping Process ◽  
Multi Robot ◽  
Map Merging

Probabilistic map merging for multi-robot RBPF-SLAM with unknown initial poses

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 205-220 ◽  
Author(s):  
Heon-Cheol Lee ◽  
Seung-Hwan Lee ◽  
Myoung Hwan Choi ◽  
Beom-Hee Lee
Keyword(s):  
Gaussian Process ◽  
Particle Filter ◽  
Computer Simulations ◽  
Simultaneous Localization And Mapping ◽  
Transformation Matrix ◽  
Localization And Mapping ◽  
Probabilistic Map ◽  
Multi Robot ◽  
Map Merging

SUMMARYThis paper addresses the map merging problem, which is the most important issue in multi-robot simultaneous localization and mapping (SLAM) using the Rao–Blackwellized particle filter (RBPF-SLAM) with unknown initial poses. The map merging is performed using the map transformation matrix and the pair of map merging bases (MMBs) of the robots. However, it is difficult to find appropriate MMBs because each robot pose is estimated under multi-hypothesis in the RBPF-SLAM. In this paper, probabilistic map merging (PMM) using the Gaussian process is proposed to solve the problem. The performance of PMM was verified by reducing errors in the merged map with computer simulations and real experiments.

scholarly journals A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6988
Author(s):  
Shuien Yu ◽  
Chunyun Fu ◽  
Amirali K. Gostar ◽  
Minghui Hu
Keyword(s):  
Multiple Robots ◽  
Working Mechanism ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Localization And Mapping ◽  
Robot Systems ◽  
Feature Based ◽  
Occupancy Grid Maps ◽  
Multi Robot ◽  
Map Merging

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.

scholarly journals The influence of the map merging order on the resulting global map in multi-robot mapping

2012 ◽  
Vol 13 (1) ◽  
pp. 22-28
Author(s):  
Ilze Andersone
Keyword(s):  
Autonomous Robot ◽  
Multiple Robots ◽  
Important Prerequisite ◽  
Robot Teams ◽  
Mapping Process ◽  
As If ◽  
Multi Robot ◽  
Map Merging

An important prerequisite for creation of an autonomous robot is the ability to create the map of the environment. While the use of robot teams becomes more and more widely used, the issue of robot coordination becomes one of the central questions to be addressed. If multiple robots are used for the exploration of the environment, their collected information has to be fused into one general global map. This problem is called map merging. In the case, when more than two robots map the environment, it is possible that the order of map merging can influence the quality of the result - the global map. However, most researches in the map merging field address the problem as if the recommended order of map merging were known. The goal of this paper is to prove that the merging order can greatly influence the resulting global map and discuss the consequences this knowledge makes in the mapping process.

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