Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

Fidel Aznar, Francisco A Pujol, Mar Pujol, Ramón Rizo and María-José Pujol

PLOS ONE, 2014, vol. 9, issue 11, 1-21

Abstract: 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(N2), 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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0112507

DOI: 10.1371/journal.pone.0112507

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