Indoor Global Localization Using Depth-Guided Photometric Edge Descriptor for Mobile Robot Navigation

Howon Cheong, Euntai Kim, Sung Kee Park

Research output: Contribution to journalArticlepeer-review

Abstract

This paper suggests a new landmark descriptor for indoor mobile robot navigation with sensor fusion and a global localization method using it. In previous research on robot pose estimation, various landmarks such as geometric features, visual local-invariant features, or objects are utilized. However, in real-world situations, there is a possibility that distinctive landmarks are insufficient or there are many similar landmarks repeated in indoor environment, which makes accurate pose estimation difficult. In this work, we suggest a new landmark descriptor, called depth-guided photometric edge descriptor (DPED), which is composed of superpixels and approximated 3D depth information of photometric vertical edge. With this descriptor, we propose a global localization method based on coarse-to-fine strategy. In the coarse step, candidate nodes are found by place recognition using our pairwise constraint-based spectral matching technique, and the robot pose is estimated with a probabilistic scan matching in the fine step. The experimental results show that our method successfully estimates the robot pose in the real-world tests even when there is a lack of distinctive features and objects.

Original languageEnglish
Article number8782605
Pages (from-to)10837-10847
Number of pages11
JournalIEEE Sensors Journal
Volume19
Issue number22
DOIs
Publication statusPublished - 2019 Nov 15

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (No. 10060086, A robot intelligence software framework as an open and self-growing integration foundation of intelligence and knowledge for personal service robots) funded by the Ministry of Trade, Industry and Energy (MI, Korea), and also supported by the National Research Council of Science and Technology (NST) grant by the Korea government (No. CRC-15-04-KIST). The associate editor coordinating the review of this article and approving it for publication was Dr. Ying Zhang.

Funding Information:
Manuscript received June 23, 2019; accepted July 21, 2019. Date of publication July 31, 2019; date of current version October 17, 2019. This work was supported by the Technology Innovation Program (No. 10060086, A robot intelligence software framework as an open and self-growing integration foundation of intelligence and knowledge for personal service robots) funded by the Ministry of Trade, Industry and Energy (MI, Korea), and also supported by the National Research Council of Science and Technology (NST) grant by the Korea government (No. CRC-15-04-KIST). The associate editor coordinating the review of this article and approving it for publication was Dr. Ying Zhang. (Corresponding author: Sung-Kee Park.) H. Cheong is with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea, and also with the Department of Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, South Korea (e-mail: howoncheong@gmail.com).

Publisher Copyright:
© 2001-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

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