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Compression of topological models and localization using the global appearance of visual information

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Luis Paya Castello
  • Sergio Cebollada
  • O Reinoso
Original languageEnglish
Title of host publication2017 IEEE International Conference on Robotics and Automation (ICRA 2017)
Place of Publication9781509046348
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
StateAccepted/In press - 1 Mar 2017
EventIEEE International Conference on Robotics and Automation (ICRA) 2017 - , Singapore


ConferenceIEEE International Conference on Robotics and Automation (ICRA) 2017


In this work, a clustering approach to obtain compact topological models of an environment is developed and evaluated. The usefulness of these models is tested by studying their utility to solve the robot localization problem subsequently. Omnidirectional visual information and global appearance descriptors are used both to create and compress the models and to estimate the position of the robot. Comparing to the methods based on the extraction and description of landmarks, global appearance approaches permit building models that can be handled and interpreted more intuitively and using relatively straightforward algorithms to estimate the position of the robot. The proposed algorithms are tested with a set of panoramic images captured with a catadioptric vision sensor in a large environment under real working conditions. The results show that it is possible to compress substantially the visual information contained in topological models to arrive to a balance between the computational cost and the accuracy of the localization process.


IEEE International Conference on Robotics and Automation (ICRA) 2017

Duration29 May 20173 Jun 2017
Location of eventSingapore
CountrySingaporeDegree of recognitionInternational event

Event: Conference

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