Representing and updating objects' identities in semantic SLAM

Or Tslil, Amit Elbaz, Tal Feiner, Avishy Carmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Simultaneous localization and mapping (SLAM) deals with localizing and mapping in unknown environment. Semantic SLAM incorporates an additional layer of objects identities and their relationships. Here we suggest representing the identity of an object in semantic SLAM as a probability distribution over the object's traits, such as labels, colors, shapes, materials, etc. Objects' identities are estimated by integrating measurements from different sensors and are distinguished based on the discrepancy between the underlying probability distributions as quantified by the Bhattacharyya distance. The semantic mapping scheme is tested both in simulation and experiment using a ground robot.

Original languageEnglish
Title of host publicationProceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780578647098
DOIs
StatePublished - 1 Jul 2020
Event23rd International Conference on Information Fusion, FUSION 2020 - Virtual, Pretoria, South Africa
Duration: 6 Jul 20209 Jul 2020

Publication series

NameProceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020

Conference

Conference23rd International Conference on Information Fusion, FUSION 2020
Country/TerritorySouth Africa
CityVirtual, Pretoria
Period6/07/209/07/20

Keywords

  • Bhattacharyya distance
  • Objects detection
  • Semantic SLAM

Fingerprint

Dive into the research topics of 'Representing and updating objects' identities in semantic SLAM'. Together they form a unique fingerprint.

Cite this