title: Learning probabilistic features for robotic navigation using laser sensors.
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(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.
authors.affiliation: Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Alicante, Spain.
authors.forename: Fidel
authors.initials: F
authors.lastname: Aznar
authors.affiliation: Departamento de Tecnología Informática y Computación, Universidad de Alicante, Alicante, Spain.
authors.forename: Francisco A
authors.initials: FA
authors.lastname: Pujol
authors.affiliation: Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Alicante, Spain.
authors.forename: Mar
authors.initials: M
authors.lastname: Pujol
authors.affiliation: Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Alicante, Spain.
authors.forename: Ramón
authors.initials: R
authors.lastname: Rizo
authors.affiliation: Departamento de Matemática Aplicada, Universidad de Alicante, Alicante, Spain.
authors.forename: María-José
authors.initials: MJ
authors.lastname: Pujol
mesh_terms: D007834:Lasers; D007858:Learning; D011336:Probability; D012371:Robotics