Deep-Learning Approach for Uncertainty Error Evaluation of Crowdsourced Trajectories and Navigation Database Generation

Yue Yu, Zhewei Liu, Shiyu Bai, Liang Chen, Ruizhi Chen

Peer Reviewed

Abstract: Ubiquitous indoor positioning technology plays an important role in providing indoor location-based services (iLBS) for the public. At this stage, crowdsourced multi-modal data fusion is regarded as an effective way to realize ubiquitous indoor positioning, especially for large-scale indoor spaces based on public daily-life trajectories and local positioning stations. Therefore, an effective uncertainty error evaluation method for daily-life trajectories is the key to generating a high-quality crowdsourced navigation database and further improving the performance of the final multi-source fusion system. To solve this problem, this paper proposes a deep-learning approach for autonomously evaluating the uncertainty error of crowdsourced daily-life trajectories, by learning and analyzing motion features extracted from pedestrian trajectories comprehensively from spatial and temporal perspectives. A novel deep-learning structure taking into account the spatiotemporal characteristics of the trajectory is modeled and related spatiotemporal features are extracted and modeled as the input vector of the proposed deep-learning structure. Real-world experimental results under generated trajectory datasets from large-scale indoor scenarios indicate that the proposed deep-learning structure can autonomously evaluate the uncertainty error of crowdsourced trajectories and realize much more accurate navigation database generation performance compared with existing state-of-the-art algorithms.
Published in: Proceedings of the 2024 International Technical Meeting of The Institute of Navigation
January 23 - 25, 2024
Hyatt Regency Long Beach
Long Beach, California
Pages: 1202 - 1214
Cite this article: Yu, Yue, Liu, Zhewei, Bai, Shiyu, Chen, Liang, Chen, Ruizhi, "Deep-Learning Approach for Uncertainty Error Evaluation of Crowdsourced Trajectories and Navigation Database Generation," Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2024, pp. 1202-1214. https://doi.org/10.33012/2024.19572
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