Paper Info
Title
Semantic Localization through Propagation of Scene Information in a Hierarchical Model
Abstract
The success of mobile robots, and particularly these coexisting with humans, relies on the ability to understand human environments. Representing the world and analysing spaces in a similar way to humans will enhance their comprehension and enable higher abstraction capabilities and interactions. The purpose of this work is to develop a localization framework that takes into account the different scenes common in a human environment and a hierarchical model of the environment. A probabilistic model for recognizing scenes is employed to determine the scene in which the robot is located. To allow that, the information about the objects and the relationships between them are considered. Besides that, a hierarchical model formed by different topological representations according to different levels of abstraction is proposed. Localization is performed at different levels to improve the localization accuracy. In this work, scene information is used to improve the localization of a mobile robot in a hierarchical model using hidden Markov models. The experiments of our framework working in real environments uphold the usefulness of the inclusion of the understanding and abstraction of the environment in localization.
Year
DOI
Venue
2019
10.1109/ECMR.2019.8870972
2019 European Conference on Mobile Robots (ECMR)
Keywords
Field
DocType
Semantic localization,robot localization,scene recognition,hierarchical modelling
Computer vision,Robot localization,Abstraction,Computer science,Artificial intelligence,Statistical model,Hidden Markov model,Robot,Hierarchical database model,Mobile robot,Comprehension
Conference
ISBN
Citations 
PageRank 
978-1-7281-3606-6
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Clara Gómez164.86
Alejandra Carolina Hernández265.54
Erik Derner335.81
Ramon Barber4103.78