Ceiling feature detection for Geo-referencing in SLAM

Tamaki Kobayashi, Masafumi Nakagawa

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

Abstract

In this study, we focused on ceiling features, such as illuminators, emergency sign boards, and Wi-Fi routers, to cancel accumulated errors in simultaneous localization and mapping. First, point cloud data in indoor spaces are acquired using a time-of-flight camera. Second, ceiling surfaces are estimated from the acquired point cloud data with the random sample consensus algorithm. Third, ceiling features are estimated to be used for reference features. Then, gravity points of the estimated features are estimated to be used for reference points. Finally, an indoor environment map is generated with the reference points. Through our experiments in indoor environments, we clarified that our methodology can detect ceiling features to be used for reference points.

Original languageEnglish
Title of host publication37th Asian Conference on Remote Sensing, ACRS 2016
PublisherAsian Association on Remote Sensing
Pages443-448
Number of pages6
Volume1
ISBN (Electronic)9781510834613
Publication statusPublished - 2016
Event37th Asian Conference on Remote Sensing, ACRS 2016 - Colombo, Sri Lanka
Duration: 2016 Oct 172016 Oct 21

Other

Other37th Asian Conference on Remote Sensing, ACRS 2016
CountrySri Lanka
CityColombo
Period16/10/1716/10/21

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Keywords

  • Geo-referencing
  • Indoor mapping
  • Random point cloud
  • SLAM
  • TOF camera

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Kobayashi, T., & Nakagawa, M. (2016). Ceiling feature detection for Geo-referencing in SLAM. In 37th Asian Conference on Remote Sensing, ACRS 2016 (Vol. 1, pp. 443-448). Asian Association on Remote Sensing.