Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models

Clemens Arth, Ruyu Liu, Jianhua Zhang, Shengyong Chen

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

Abstract

In this paper, we address the topic of outdoor localization and tracking using monocular camera setups with poor GPS priors. We leverage 2.5 D building maps, which are freely available from open-source databases such as OpenStreetMap.
The main contributions of our work are a fast initialization method and a non-linear optimization scheme. The initialization upgrades a visual SLAM reconstruction with an absolute scale. The non-linear optimization uses the 2.5 D building model footprint, which further improves the tracking accuracy and the scale estimation. A pose optimization step relates the vision-based camera pose estimation from SLAM to the position information received through GPS, in order to fix the common problem of drift. We evaluate our approach on a set of challenging scenarios. The experimental results show that our approach achieves improved accuracy and robustness with an advantage in run-time over previous setups.
Original languageEnglish
Title of host publication2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)978-1-7281-0987-9
DOIs
Publication statusPublished - 2019
Event2019 IEEE International Symposium on Mixed and Augmented Reality - Bejing, China
Duration: 14 Oct 201918 Oct 2019

Conference

Conference2019 IEEE International Symposium on Mixed and Augmented Reality
Abbreviated titleISMAR 2019
CountryChina
CityBejing
Period14/10/1918/10/19

Fingerprint

Global positioning system
Cameras

Cite this

Arth, C., Liu, R., Zhang, J., & Chen, S. (2019). Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models. In 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISMAR.2019.00016

Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models. / Arth, Clemens; Liu, Ruyu; Zhang, Jianhua; Chen, Shengyong.

2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Institute of Electrical and Electronics Engineers, 2019.

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

Arth, C, Liu, R, Zhang, J & Chen, S 2019, Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models. in 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Institute of Electrical and Electronics Engineers, 2019 IEEE International Symposium on Mixed and Augmented Reality, Bejing, China, 14/10/19. https://doi.org/10.1109/ISMAR.2019.00016
Arth C, Liu R, Zhang J, Chen S. Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models. In 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Institute of Electrical and Electronics Engineers. 2019 https://doi.org/10.1109/ISMAR.2019.00016
Arth, Clemens ; Liu, Ruyu ; Zhang, Jianhua ; Chen, Shengyong. / Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models. 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Institute of Electrical and Electronics Engineers, 2019.
@inproceedings{bf55e9ece2624c83b55e1107094941d3,
title = "Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models",
abstract = "In this paper, we address the topic of outdoor localization and tracking using monocular camera setups with poor GPS priors. We leverage 2.5 D building maps, which are freely available from open-source databases such as OpenStreetMap.The main contributions of our work are a fast initialization method and a non-linear optimization scheme. The initialization upgrades a visual SLAM reconstruction with an absolute scale. The non-linear optimization uses the 2.5 D building model footprint, which further improves the tracking accuracy and the scale estimation. A pose optimization step relates the vision-based camera pose estimation from SLAM to the position information received through GPS, in order to fix the common problem of drift. We evaluate our approach on a set of challenging scenarios. The experimental results show that our approach achieves improved accuracy and robustness with an advantage in run-time over previous setups.",
author = "Clemens Arth and Ruyu Liu and Jianhua Zhang and Shengyong Chen",
year = "2019",
doi = "10.1109/ISMAR.2019.00016",
language = "English",
booktitle = "2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

TY - GEN

T1 - Towards SLAM-based Outdoor Localization using Poor GPS and 2.5D Building Models

AU - Arth, Clemens

AU - Liu, Ruyu

AU - Zhang, Jianhua

AU - Chen, Shengyong

PY - 2019

Y1 - 2019

N2 - In this paper, we address the topic of outdoor localization and tracking using monocular camera setups with poor GPS priors. We leverage 2.5 D building maps, which are freely available from open-source databases such as OpenStreetMap.The main contributions of our work are a fast initialization method and a non-linear optimization scheme. The initialization upgrades a visual SLAM reconstruction with an absolute scale. The non-linear optimization uses the 2.5 D building model footprint, which further improves the tracking accuracy and the scale estimation. A pose optimization step relates the vision-based camera pose estimation from SLAM to the position information received through GPS, in order to fix the common problem of drift. We evaluate our approach on a set of challenging scenarios. The experimental results show that our approach achieves improved accuracy and robustness with an advantage in run-time over previous setups.

AB - In this paper, we address the topic of outdoor localization and tracking using monocular camera setups with poor GPS priors. We leverage 2.5 D building maps, which are freely available from open-source databases such as OpenStreetMap.The main contributions of our work are a fast initialization method and a non-linear optimization scheme. The initialization upgrades a visual SLAM reconstruction with an absolute scale. The non-linear optimization uses the 2.5 D building model footprint, which further improves the tracking accuracy and the scale estimation. A pose optimization step relates the vision-based camera pose estimation from SLAM to the position information received through GPS, in order to fix the common problem of drift. We evaluate our approach on a set of challenging scenarios. The experimental results show that our approach achieves improved accuracy and robustness with an advantage in run-time over previous setups.

U2 - 10.1109/ISMAR.2019.00016

DO - 10.1109/ISMAR.2019.00016

M3 - Conference contribution

BT - 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

PB - Institute of Electrical and Electronics Engineers

ER -