Data-Driven Measurement Models for Active Localization in Sparse Environments

Ian Abraham, Anastasia Mavrommati, Todd D. Murphey

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

6 Scopus citations

Abstract

We develop an algorithm to explore an environment to generate a measurement model for use in future localization tasks. Ergodic exploration with respect to the likelihood of a particular class of measurement (e.g., a contact detection measurement in tactile sensing) enables construction of the measurement model. Exploration with respect to the information density based on the data-driven measurement model enables localization. We test the two-stage approach in simulations of tactile sensing, illustrating that the algorithm is capable of identifying and localizing objects based on sparsely distributed binary contacts. Comparisons with our method show that visiting low probability regions lead to acquisition of new information rather than increasing the likelihood of known information. Experiments with the Sphero SPRK robot validate the efficacy of this method for collision-based estimation and localization of the environment.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XIV
EditorsHadas Kress-Gazit, Siddhartha S. Srinivasa, Tom Howard, Nikolay Atanasov
PublisherMIT Press Journals
ISBN (Print)9780992374747
DOIs
StatePublished - 2018
Externally publishedYes
Event14th Robotics: Science and Systems, RSS 2018 - Pittsburgh, United States
Duration: Jun 26 2018Jun 30 2018

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X

Conference

Conference14th Robotics: Science and Systems, RSS 2018
Country/TerritoryUnited States
CityPittsburgh
Period6/26/186/30/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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