Abstract:
Using crowdsourced visual and inertial sensor data for indoor mapping has attracted much attention in recent years. Nevertheless, the opportunities and challenges of indo...Show MoreMetadata
Abstract:
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.
Published in: 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
Date of Conference: 11-14 April 2016
Date Added to IEEE Xplore: 28 April 2016
ISBN Information: