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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4240708?pdf=render |
| _version_ | 1811236350794399744 |
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| author | Fidel Aznar Francisco A Pujol Mar Pujol Ramón Rizo María-José Pujol |
| author_facet | Fidel Aznar Francisco A Pujol Mar Pujol Ramón Rizo María-José Pujol |
| author_sort | Fidel Aznar |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T12:07:35Z |
| format | Article |
| id | doaj.art-0b96591ee8de40e09fd36b52f49608c2 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T12:07:35Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0b96591ee8de40e09fd36b52f49608c22022-12-22T03:33:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01911e11250710.1371/journal.pone.0112507Learning probabilistic features for robotic navigation using laser sensors.Fidel AznarFrancisco A PujolMar PujolRamón RizoMaría-José PujolSLAM 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.http://europepmc.org/articles/PMC4240708?pdf=render |
| spellingShingle | Fidel Aznar Francisco A Pujol Mar Pujol Ramón Rizo María-José Pujol Learning probabilistic features for robotic navigation using laser sensors. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC4240708?pdf=render |
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