Context-aware switching between localisation methods for robust robot navigation: A self-supervised learning approach

Raul Guilherme, Francisco Marques, Andre Lourenco, Ricardo Mendonca, Pedro Santana, Jose Barata

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

1 Citation (Scopus)

Abstract

This paper presents an incremental learning mechanism for context-aware switching between localisation methods which are available to the robots control system (e.g., GPS-based, map-based). The goal is to avoid the cumbersome and error prone manual mapping between localisation methods and environmental contexts. At each moment, the system determines which localisation method is performing best by comparison with the motion estimates produced by an odometer, assumed as accurate in the short-time. Then, the best performing method is associated to the current environmental context, which is defined by a novel descriptor built from the local occupancy grid. The result of this instance-based learning process is used online to estimate which localisation method performs the best in the current environmental context. The switching process is facilitated by the use of the de facto standard Robot Operating System (ROS) framework. The system was instantiated in a differential-wheeled robot equipped with a short-range 2-D laser scanner, and successfully validated on a set of field trials.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4356-4361
Number of pages6
ISBN (Electronic)978-1-5090-1897-0
DOIs
Publication statusPublished - 6 Feb 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period9/10/1612/10/16

Keywords

  • URBAN CHALLENGE

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