Cargando…
Learning Probabilistic Features for Robotic Navigation Using Laser Sensors
SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its env...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4240708/ https://www.ncbi.nlm.nih.gov/pubmed/25415377 http://dx.doi.org/10.1371/journal.pone.0112507 |
_version_ | 1782345761129758720 |
---|---|
author | Aznar, Fidel Pujol, Francisco A. Pujol, Mar Rizo, Ramón Pujol, María-José |
author_facet | Aznar, Fidel Pujol, Francisco A. Pujol, Mar Rizo, Ramón Pujol, María-José |
author_sort | Aznar, Fidel |
collection | PubMed |
description | SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N (2)), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used. |
format | Online Article Text |
id | pubmed-4240708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-42407082014-11-26 Learning Probabilistic Features for Robotic Navigation Using Laser Sensors Aznar, Fidel Pujol, Francisco A. Pujol, Mar Rizo, Ramón Pujol, María-José PLoS One Research Article SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N (2)), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used. Public Library of Science 2014-11-21 /pmc/articles/PMC4240708/ /pubmed/25415377 http://dx.doi.org/10.1371/journal.pone.0112507 Text en © 2014 Aznar et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Aznar, Fidel Pujol, Francisco A. Pujol, Mar Rizo, Ramón Pujol, María-José Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
title | Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
title_full | Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
title_fullStr | Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
title_full_unstemmed | Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
title_short | Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
title_sort | learning probabilistic features for robotic navigation using laser sensors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4240708/ https://www.ncbi.nlm.nih.gov/pubmed/25415377 http://dx.doi.org/10.1371/journal.pone.0112507 |
work_keys_str_mv | AT aznarfidel learningprobabilisticfeaturesforroboticnavigationusinglasersensors AT pujolfranciscoa learningprobabilisticfeaturesforroboticnavigationusinglasersensors AT pujolmar learningprobabilisticfeaturesforroboticnavigationusinglasersensors AT rizoramon learningprobabilisticfeaturesforroboticnavigationusinglasersensors AT pujolmariajose learningprobabilisticfeaturesforroboticnavigationusinglasersensors |