Learning Probabilistic Features for Robotic Navigation Using Laser Sensors |
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Authors: | Fidel Aznar Francisco A. Pujol Mar Pujol Ramón Rizo María-José Pujol |
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Affiliation: | 1Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Alicante, Spain;2Departamento de Tecnología Informática y Computación, Universidad de Alicante, Alicante, Spain;3Departamento de Matemática Aplicada, Universidad de Alicante, Alicante, Spain;Nanjing University of Aeronautic and Astronautics, China |
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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. |
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