Paper Info
Title
A Neuro-Inspired Approach To Solve A Simultaneous Location And Mapping Task Using Shared Information In Multiple Robots Systems
Abstract
This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.
Year
DOI
Venue
2018
10.1109/ICARCV.2018.8581270
2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV)
Field
DocType
ISSN
Computer vision,Computer science,Navigation system,Control engineering,Artificial intelligence,Simultaneous localization and mapping,Merge (version control),Robot
Conference
2474-2953
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Matheus Chaves Menezes100.34
Edison Pignaton Freitas25611.25
Sen Cheng300.68
Alexandre César Muniz De Oliveira4838.30
Paulo Rogério de Almeida Ribeiro500.68