research-article

Ubiquitous real-time geo-spatial localization

Authors:

Alper YilmazAuthors Info & Claims

ISA '16: Proceedings of the Eighth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness

Pages 1 - 10

https://doi.org/10.1145/3005422.3005426

Published: 31 October 2016 Publication History

Abstract

Rapidly growing technologies like autonomous navigation require accurate geo-localization in both outdoor and indoor environments. GNSS based outdoor localization has limitation of accuracy, which deteriorates in urban canyons, forested region and is unavailable indoors. Technologies like RFID, UWB, WiFi are used for indoor localization. These suffer limitations of high infrastructure costs, and signal transmission issues like multi-path, and frequent replacement of transciever batteries. We propose an alternative to localize an individual or a vehcile that is moving inside or outside a building. Instead of mobile RF transceivers, we utilize a sensor suite that includes a video camera and an inertial measurement unit. We estimate a motion trajectory of this sensor suite using Visual Odometery. Instead of preinstalled transceivers, we use GIS map for outdoors, or a BIM model for indoors. The transport layer in GIS map or navigable paths in BIM are abstracted as a graph structure. The geo-location of the mobile platform is inferred by first localizing its trajectory. We introduce an adaptive probabilistic inference approach to search for this trajectory in the entire map with no initialization information. Using an effective graph traversal spawn-and-prune strategy, we can localize the mobile platform in real-time. In comparison to other technologies, our approach requires economical sensors and the required map data is typically available in the public domain. Additionally, unlike other technologies which function exclusively indoors or outdoors, our approach functions in both environments. We demonstrate our approach on real world examples of both indoor and outdoor locations.

References

[1]

P. Aggarwal, D. Thomas, L. Ojeda, and J. Borenstein. Map matching and heuristic elimination of gyro drift for personal navigation systems in GPS-denied conditions. Measurement Science and Technology, 22(2):025205, 2011.

[2]

I. P. Alonso, D. F. Llorca, M. Gavilan, S. a. Pardo, M. a. Garcia-Garrido, L. Vlacic, and M. a. Sotelo. Accurate Global Localization Using Visual Odometry and Digital Maps on Urban Environments. Ieee Transactions on Intelligent Transportation Systems, 13(4):1535--1545, 2012.

Digital Library

[3]

S. Ardeshir, A. R. Zamir, A. Torroella, and M. Shah. GIS-Assisted Object Detection and Geospatial Localization. In D. Fleet and T. Pajdla, editors, European Conference on Computer Vision, pages 602--617. Springer, 2014.

[4]

M. S. Asher, S. J. Stafford, R. J. Bamberger, A. Q. Rogers, D. Scheidt, and R. Chalmers. Radionavigation Alternatives for US Army Ground Forces in GPS Denied Environments. In International Technical Meeting of The Institute of Navigation, pages 508--532, San Diego, CA, 2011. ION.

[5]

H. Badino, D. Huber, and T. Kanade. Visual topometric localization. IEEE Intelligent Vehicles Symposium, Proceedings, pages 794--799, 2011.

[6]

M. Bosse, P. Newman, J. Leonard, M. Soika, W. Feiten, and S. Teller. An Atlas framework for scalable mapping. 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), 2(September):1899--1906, 2003.

[7]

M. Brubaker, A. Geiger, and R. Urtasun. Map-Based Probabilistic Visual Self-Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, (99):1--1, 2015.

Digital Library

[8]

J. Civera, O. G. Grasa, A. J. Davison, and J. M. M. Montiel. 1-Point RANSAC for EKF Filtering. Application to Real-Time Structure from Motion and Visual Odometry. Journal of Field Robotics, 27(5):609--631, 2010.

Digital Library

[9]

L. E. Clement, V. Peretroukhin, J. Lambert, and J. Kelly. The Battle for Filter Supremacy: A Comparative Study of the Multi-State Constraint Kalman Filter and the Sliding Window Filter. In Proceedings -2015 12th Conference on Computer and Robot Vision, CRV 2015, pages 23--30. IEEE, 2015.

Digital Library

[10]

J. Dong and S. Soatto. Domain-Size Pooling in Local Descriptors: DSP-SIFT. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), number 1, pages 5097--5106. IEEE, 2015.

[11]

M. Emery and M. Denko. Ieee 802.11 wlan based real-time location tracking in indoor and outdoor environments. In Electrical and Computer Engineering, 2007. CCECE 2007. Canadian Conference on, pages 1062--1065, April 2007.

[12]

P. Enge. Local area augmentation of gps for the precision approach of aircraft. Proceedings of the IEEE, 87(1):111--132, Jan 1999.

[13]

J. Engel, T. Sch, and D. Cremers. LSD-SLAM: Large-Scale Direct Monocular SLAM. In European Conference on Computer Vision, pages 1--16, 2014.

[14]

G. Floros, B. Van Der Zander, and B. Leibe. OpenStreetSLAM: Global vehicle localization using OpenStreetMaps. Proceedings --- IEEE International Conference on Robotics and Automation, pages 1054--1059, 2013.

[15]

M. Hentschel and B. Wagner. Autonomous robot navigation based on OpenStreetMap geodata. Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, pages 1645--1650, 2010.

[16]

B. Kitt, A. Geiger, and H. Lategahn. Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. In IEEE Intelligent Vehicles Symposium, Proceedings, pages 486--492, 2010.

[17]

R. Kumar, M. G. Petovello, and C. Engineering. A Novel GNSS Positioning Technique for Improved Accuracy in Urban Canyon Scenarios using 3D City Model. In ION GNSS+, pages 1--10, Tampa, FL, 2014. Institute of Navigation.

[18]

H. Lategahn, A. Geiger, and B. Kitt. Visual SLAM for autonomous ground vehicles. Proceedings --- IEEE International Conference on Robotics and Automation, pages 1732--1737, 2011.

[19]

J. Levinson, M. Montemerlo, and S. Thrun. Map-Based Precision Vehicle Localization in Urban Environments. Robotics: Science and Systems III, pages 121--128, 2008.

[20]

G. Ligorio and A. Sabatini. Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation. Sensors, 13(2):1919--1941, 2013.

[21]

D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2):91--110, 2004.

Digital Library

[22]

R. Mautz. Indoor Positioning Technologies. (February 2012):127, 2012.

[23]

A. I. Mourikis and S. I. Roumeliotis. A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation. In IEEE Int. Conf. on Robotics and Automation (ICRA), number April, pages 3565--3572, Rome, 2007. IEEE.

[24]

D. T. Nguyen, H. Chi, and M. City. A Novel Chamfer Template Matching Method Using Variational Mean Field. In Computer Vision and Pattern Recognition, pages 2425--2432, 2014.

Digital Library

[25]

I. Parra, M. Ángel Sotelo, D. F. Llorca, C. Fernández, A. Llamazares, N. Hernández, and I. García. Visual odometry and map fusion for GPS navigation assistance. In Proceedings of 2011 IEEE International Symposium on Industrial Electronics, pages 832--837, 2011.

[26]

R. Paul and P. Newman. FAB-MAP 3D: Topological mapping with spatial and visual appearance. Proceedings --- IEEE International Conference on Robotics and Automation, pages 2649--2656, 2010.

[27]

K. M. Pesyna, R. W. Heath, and T. E. Humphreys. Centimeter Positioning with a Smartphone-Quality GNSS Antenna. In ION GNSS+, pages 1--10, Tampa, FL, 2014. Institute of Navigation.

[28]

M. Psiaki and H. Jung. Extended Kalman filter methods for tracking weak GPS signals. In ION GNSS+, pages 2539--2553, Portland, Oregon, 2002. ION.

[29]

A. Ranganathan, E. Menegatti, and F. Dellaert. Bayesian inference in the space of topological maps. IEEE Transactions on Robotics, 22(1):92--107, 2006.

Digital Library

[30]

D. Scaramuzza and R. Siegwart. Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Transactions on Robotics, 24(5):1015--1026, 2008.

Digital Library

[31]

S. Shekhar and H. Xiong. Encyclopedia of GIS. Springer Publishing Company, Incorporated, 1st edition, 2007.

Digital Library

[32]

D. Strelow and S. Singh. Motion estimation from image and inertial measurements. The International Journal of Robotics Research, 23(12):1157--1195, 2004.

[33]

S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L.-E. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van Niekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. Nefian, and P. Mahoney. Stanley: The robot that won the darpa grand challenge: Research articles. J. Robot. Syst., 23(9):661--692, Sept. 2006.

Digital Library

[34]

L. Wei, C. Cappelle, Y. Ruichek, and F. Zann. GPS and Stereovision-Based Visual Odometry: Application to Urban Scene Mapping and Intelligent Vehicle Localization. International Journal of Vehicular Technology, 2011:1--17, 2011.

[35]

B. Williams and I. Reid. On combining visual SLAM and visual odometry. Proceedings --- IEEE International Conference on Robotics and Automation, pages 3494--3500, 2010.

[36]

A. D. Wu, E. N. Johnson, M. Kaess, F. Dellaert, and G. Chowdhary. Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors. Journal of Aerospace Information Systems, 10(4):172--186, 2013.

[37]

J. Yang, K. Yu, and T. Huang. Efficient Highly Over-Complete Sparse Coding using a Mixture Model. In European Conference on Computer Vision, pages 113--126, Crete, 2010.

Digital Library

[38]

A. Yilmaz, O. Javed, and M. Shah. Object tracking: A Survey. ACM Computing Surveys, 38(4):1--44, dec 2006.

Digital Library

[39]

M. Zaman. High precision relative localization using a single camera. In Robotics and Automation, 2007 IEEE International Conference on, pages 3908--3914, April 2007.

Cited By

Zha BYilmaz A(2023)Subgraph Learning for Topological Geolocalization with Graph Neural NetworksSensors10.3390/s2311509823:11(5098)Online publication date: 26-May-2023
https://doi.org/10.3390/s23115098
Yi SWorrall SNebot E(2021)Integrating Vision, Lidar and GPS Localization in a Behavior Tree Framework for Urban Autonomous Driving2021 IEEE International Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC48978.2021.9564875(3774-3780)Online publication date: 19-Sep-2021
https://dl.acm.org/doi/10.1109/ITSC48978.2021.9564875
Yasuda YMartins LCappabianco F(2020)Autonomous Visual Navigation for Mobile RobotsACM Computing Surveys10.1145/336896153:1(1-34)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3368961
Show More Cited By

Index Terms

Ubiquitous real-time geo-spatial localization
1. Applied computing
  1. Computers in other domains
    1. Cartography

Recommendations

A survey on indoor positioning security and privacy
Abstract
With rising demand for indoor location-based services (LBS) such as location-based marketing, mobile navigation, etc., there has been considerable interest in indoor positioning methods as well as their security and privacy. Current ...
Indoor Real-Time Localization by Mitigating Multipath Signals
2021 IEEE Wireless Communications and Networking Conference (WCNC)
Indoor localization using WiFi signal has received great attentions since it is ubiquitous. So, in this paper we propose the design, implementation and evaluation of a real-time indoor localization system using commodity WiFi signal. The proposed system ...
Autonomous Localization and Navigation for Agricultural Robots in Greenhouse
Abstract
Agricultural robots are an effective way to solve the increasingly prominent shortage of agricultural labor. To meet the needs of practical applications, agricultural robots must be able to locate autonomously and move from one place to another ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ISA '16: Proceedings of the Eighth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness

October 2016

56 pages

ISBN:9781450345859

DOI:10.1145/3005422

Editors:
Muhammad Aamir Cheema
Monash University, Australia
,
Mohammed Eunus Ali
Bangladesh University of Engineering and Technology, Bangladesh
,
Shiyu Yang
The University of New South Wales, Australia

Copyright © 2016 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]

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGSPATIAL'16

Sponsor:

SIGSPATIAL

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31, 2016

California, Burlingame

Acceptance Rates

ISA '16 Paper Acceptance Rate 5 of 7 submissions, 71%;

Overall Acceptance Rate 5 of 7 submissions, 71%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
308
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zha BYilmaz A(2023)Subgraph Learning for Topological Geolocalization with Graph Neural NetworksSensors10.3390/s2311509823:11(5098)Online publication date: 26-May-2023
https://doi.org/10.3390/s23115098
Yi SWorrall SNebot E(2021)Integrating Vision, Lidar and GPS Localization in a Behavior Tree Framework for Urban Autonomous Driving2021 IEEE International Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC48978.2021.9564875(3774-3780)Online publication date: 19-Sep-2021
https://dl.acm.org/doi/10.1109/ITSC48978.2021.9564875
Yasuda YMartins LCappabianco F(2020)Autonomous Visual Navigation for Mobile RobotsACM Computing Surveys10.1145/336896153:1(1-34)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3368961
Wei JKoroglu MZha BYilmaz A(2019)Pedestrian Localization on Topological Maps with Neural Machine Translation Network2019 IEEE SENSORS10.1109/SENSORS43011.2019.8956924(1-4)Online publication date: Oct-2019
https://doi.org/10.1109/SENSORS43011.2019.8956924
Landsiedel CWollherr D(2017)Global localization of 3D point clouds in building outline maps of urban outdoor environmentsInternational Journal of Intelligent Robotics and Applications10.1007/s41315-017-0038-21:4(429-441)Online publication date: 22-Nov-2017
https://doi.org/10.1007/s41315-017-0038-2

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents