Abstract
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.
Highlights
Autonomous driving technologies have been developing rapidly in recent decades
(2) what is around me [3]? These two problems are intertwined and must be solved simultaneously, since the accuracy of vehicle localization directly affects the accuracy of mapping and vice versa. This requirement has driven forward the research on simultaneous localization and mapping (SLAM), which has attracted a lot of attention from both the robotic and automotive communities [4,5]
To supplement the above review works on multi-robot SLAM and provide the readers with a more are the most typical forms of map representation in the relevant literature, and there have been a lot of relevant research results
Summary
Autonomous driving technologies have been developing rapidly in recent decades. Environment perception, path planning, and motion control are considered the three core technologies that enable autonomous driving [1,2]. Ben-Tzvi [2] summarized three key problems existing in multi-robot systems: mapping, localization, and motion planning, but this work lacks a systematic introduction to map merging. This paper mainly reviews the map-merging methods for summarized three key problems existing in multi-robot systems: mapping, localization, and motion these three types of maps. To supplement the above review works on multi-robot SLAM and provide the readers with a more are the most typical forms of map representation in the relevant literature, and there have been a lot of relevant research results. Sensors other maps, such as appearance-based maps, random probability maps, and semantic maps, are not widely used in SLAM applications and are not discussed in this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
