Indoor Tracking Using Crowdsourced Maps

Jiang Dong, Yu Xiao, Zhonghong Ou, Yong Cui, Antti Ylä-Jääski

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

9 Citations (Scopus)


Using crowdsourced visual and inertial sensor data for indoor mapping has attracted much attention in recent years. Nevertheless, the opportunities and challenges of indoor tracking using crowdsourced maps have not been fully explored. In this work, we aim at tackling the challenges due to incomplete obstacle information in crowdsourced indoor maps, especially at the initialization stage of crowdsourcing. We propose a novel solution for particle-filtering-based indoor tracking, using the crowdsourced maps derived from image-based 3D point clouds. Our solution enhances particle filtering with density-based collision detection and history-based particle regeneration. Evaluation with real user traces demonstrates that our solution outperforms the state-of-the-art. In particular, it reduces the average distance error of indoor tracking by 47% when using crowdsourced 3D point clouds.

Original languageEnglish
Title of host publicationInformation Processing in Sensor Networks (IPSN), 2016 15th ACM/IEEE International Conference on
Number of pages6
ISBN (Electronic)978-1-5090-0802-5
Publication statusPublished - 26 Apr 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Processing in Sensor Networks - Vienna, Austria
Duration: 11 Apr 201614 Apr 2016
Conference number: 15


ConferenceInternational Conference on Information Processing in Sensor Networks
Abbreviated titleIPSN

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