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Poster abstract: Improving RGB-D SLAM using Wi-Fi

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

4 Scopus citations

Abstract

Simultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is essential for robots to autonomously navigate. A variety of algorithms using many different sensors such as RGB-D cameras, laser range finders, ultrasonic sensors and others have been proposed to perform SLAM. However, these algorithms face common challenges are that of computational complexity, wrong loop closure detection and failure to localize correctly when robot loses state (kidnapped robot problem). In this work, we utilize Wi-Fi signal strength sensing to aid the SLAM process in indoor environments and address the challenges mentioned above.

Original languageEnglish
Title of host publicationProceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017
PublisherAssociation for Computing Machinery, Inc
Pages317-318
Number of pages2
ISBN (Electronic)9781450348904
DOIs
StatePublished - Apr 18 2017
Event16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017 - Pittsburgh, United States
Duration: Apr 18 2017Apr 20 2017

Publication series

NameProceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017

Conference

Conference16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017
Country/TerritoryUnited States
CityPittsburgh
Period04/18/1704/20/17

Keywords

  • Mapping
  • Perception
  • Robotics
  • SLAM
  • WiFi

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