Robot semantic place recognition based on deep belief networks and a direct use of tiny images
type: Distribution
access URL: https://theses.hal.science/tel-00960289
format: HTML
issued: 2026-05-06T00:31:53.254049
modified: 2026-05-06T00:31:53.202712

Robot semantic place recognition based on deep belief networks and a direct use of tiny images
type: Dataset
contact point: Organization
  fn: CCSD
keyword: semantic-places-recognition
keyword: bag-of-words
keyword: theses
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: infoeu-reposemanticsdoctoralthesis
keyword: sac-de-mots
keyword: restrict-ed-boltzmann-machines
keyword: deep-belief-networks
keyword: regression-softmax
keyword: machine-de-boltzmann-restreinte
keyword: softmax-regression
keyword: energy-based-models
keyword: modeles-bases-sur-lenergie
keyword: architecture-profonde
keyword: reconnaissance-de-lieux-semantiques
landing page: https://theses.hal.science/tel-00960289
description: Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ``semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is usually addressed as a supervised learning problem. The question of semantic places recognition in robotics - the ability to recognize the semantic category of a place to which scene belongs to - is therefore a major requirement for the future of autonomous robotics. It is indeed required for an autonomous service robot to be able to recognize the environment in which it lives and to easily learn the organization of this environment in order to operate and interact successfully. To achieve that goal, different methods have been already proposed, some based on the identification of objects as a prerequisite to the recognition of the scenes, and some based on a direct description of the scene characteristics. If we make the hypothesis that objects are more easily recognized when the scene in which they appear is identified, the second approach seems more suitable. It is however strongly dependent on the nature of the image descriptors used, usually empirically derived from general considerations on image coding.Compared to these many proposals, another approach of image coding, based on a more theoretical point of view, has emerged the last few years. Energy-based models of feature extraction based on the principle of minimizing the energy of some function according to the quality of the reconstruction of the image has lead to the Restricted Boltzmann Machines (RBMs) able to code a…
identifier: NNT: 2012PA112298
issued: 2026-05-06T00:31:53.219336
language: en
modified: 2026-05-06T00:31:53.219341

test_moissonnage_selune
type: Agent