DOI: 10.4018/IJERTCS.2020070104 International Journal of Embedded and Real-Time Communication Systems Volume 11 • Issue 3 • July-September 2020 Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 67 A Simplistic Approach for Lightweight Multi-Agent SLAM Algorithm Anton Filatov, Saint Petersburg Electrotechnical University, Russia Kirill Krinkin, Saint Petersburg Electrotechnical University, Russia ABSTRACT Limitation of computational resources is a challenging problem for moving agents that launch such algorithms as simultaneous localization and mapping (SLAM). To increase the accuracy on limited resources one may add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. In this article, the state-of-the-art in multi-agent SLAM algorithms is presented, and an approach that extends laser 2D single hypothesis SLAM for multiple agents is introduced. The article contains a description of problems that are faced in front of a developer of such approach including questions about map merging, relative pose calculation, and roles of agents. KEywoRdS Initial Pose, Laser Scan, Localization, Low-Cost Platforms, Map Merging, Mapping, Scan Matcher INTRodUCTIoN SLAM problem is a situation where a mobile platform, being placed in an unknown environment, has to build a map and find its location simultaneously. There are many algorithms that are applicable to mobile moving platforms, such as a robot vacuum cleaner, a reconnaissance drone, or even a rover. However, the process of building a map can be accelerated if several agents are used at the same time, where each explores its part of an environment, and in the future, the individual parts can be combined into one picture. The SLAM problem that is being solved by several agents is referred to as multi-agent SLAM problem further. The paper extends the work (Filatov, 2019), the most important of which questions are described below. The first part of this paper continues the work of (Krinkin, 2017) and presents a brief description of existing multi-agent SLAM algorithms, describes most common approaches and shows their advantages and disadvantages. It describes the approaches how to extend a single-agent SLAM algorithm to the multi-agent case and. It also demonstrates the approaches that were initially based on a multi-agent architecture. Thus, the high-level description of state-of-the-art approaches is presented in this paper as opposed to the previous paper which focused on doing a survey of several algorithms. The second part of this paper covers the issue of map merging (a problem about combining results of several agents). Each existing algorithm solves this problem in its own way that is most commonly based on the architecture of current approach. The paper presents the high-level solution to this problem. The third part is an intuitive extension of a laser 2D grid based single-agent SLAM algorithm. The common idea of the suggested algorithm is to fulfill two statements: