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Indoor localization using SLAM in parallel with a natural marker detector

Authors:

Lucas Teixeira,

Alberto B. Raposo,

Marcelo GattassAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 27 - 33

https://doi.org/10.1145/2480362.2480370

Published: 18 March 2013 Publication History

Abstract

Indoor localization poses is a challenge to computer vision research, since one may not make use of GPS-based devices. A classic approach commonly used in museums, research institutes, etc, is the use of fiducial marker to track the users position. However, this approach is intrusive into the ambient and not always possible. A possible solution would be natural marker detection, but algorithms for this, such as SURF, have not yet achieved real-time performance. A promising approach is a Visual Simultaneous Localization and Mapping (VSLAM) algorithm, which, starting from a known position, is capable of generating a map of the surrounding environment in portable systems. The problem of SLAM algorithms is theirs error accumulation that builds up during the movement. This work presents an algorithm to locate 3D positions in non-instrumented indoor environments using a web camera. We define a hybrid approach, using a pattern-recognition method to reinitialize whenever possible a VSLAM algorithm. An implementation of the proposed algorithm use well-known computer vision algorithms, such as SURF and Davison's SLAM. In addition, tests were made on datasets from walks inside a room. Results indicate that our approach is better than a fiducial marker tracking and pure SLAM tracking in our test environment.

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Cited By

Raza ALolic LAkhter SLiut M(2021)Comparing and Evaluating Indoor Positioning Techniques2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN51156.2021.9662632(1-8)Online publication date: 29-Nov-2021
https://doi.org/10.1109/IPIN51156.2021.9662632
Zhang YWang ZSong RYan CQi Y(2021)Detection-by-tracking of traffic signs in videosApplied Intelligence10.1007/s10489-021-02838-w52:7(8226-8242)Online publication date: 23-Oct-2021
https://doi.org/10.1007/s10489-021-02838-w
Morar AMoldoveanu AMocanu IMoldoveanu FRadoi IAsavei VGradinaru AButean A(2020)A Comprehensive Survey of Indoor Localization Methods Based on Computer VisionSensors10.3390/s2009264120:9(2641)Online publication date: 6-May-2020
https://doi.org/10.3390/s20092641
Show More Cited By

Index Terms

Indoor localization using SLAM in parallel with a natural marker detector
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding
  2. Machine learning

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cover image ACM Conferences

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

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SAC '13

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SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

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SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Raza ALolic LAkhter SLiut M(2021)Comparing and Evaluating Indoor Positioning Techniques2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN51156.2021.9662632(1-8)Online publication date: 29-Nov-2021
https://doi.org/10.1109/IPIN51156.2021.9662632
Zhang YWang ZSong RYan CQi Y(2021)Detection-by-tracking of traffic signs in videosApplied Intelligence10.1007/s10489-021-02838-w52:7(8226-8242)Online publication date: 23-Oct-2021
https://doi.org/10.1007/s10489-021-02838-w
Morar AMoldoveanu AMocanu IMoldoveanu FRadoi IAsavei VGradinaru AButean A(2020)A Comprehensive Survey of Indoor Localization Methods Based on Computer VisionSensors10.3390/s2009264120:9(2641)Online publication date: 6-May-2020
https://doi.org/10.3390/s20092641
Sampaio JEvangelista RFernandes LWainwright RCorchado JBechini AHong J(2015)Determining the location of buildings given a single picture, environment maps and inaccurate GPS coordinatesProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695810(542-547)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695810

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