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 paperpeer-review


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
Publication statusPublished - 2019
Event2019 IEEE International Symposium on Mixed and Augmented Reality: ISMAR 2019 - Bejing, China
Duration: 14 Oct 201918 Oct 2019


Conference2019 IEEE International Symposium on Mixed and Augmented Reality
Abbreviated titleISMAR 2019

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