Probabilistic constraints for robot localization

Marco Correia, Olga Meshcheryakova, Pedro Alexandre da Costa Sousa, Jorge Cruz

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

Abstract

In robot localization problems, uncertainty arises from many factors and must be considered together with the model constraints. Probabilistic robotics is the classical approach for dealing with hard robotic problems that relies on probability theory. This work describes the application of probabilistic constraint techniques in the context of probabilistic robotics to solve robot localization problems. Instead of providing the most probable position of the robot, the approach characterizes all positions consistent with the model and their probabilities (in accordance with the underlying uncertainty). It relies on constraint programming to get a tight covering of the consistent regions combined with Monte Carlo integration techniques that benefit from such reduction of the sampling space.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PublisherSpringer-Verlag
Pages480-486
Number of pages7
Volume9273
ISBN (Electronic)978-3-319-23485-4
DOIs
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9273
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015
CountryPortugal
CityCoimbra
Period8/09/1511/09/15

Keywords

  • Constraint programming
  • Probabilistic robotic
  • Robot localization

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