NDLI: Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation

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Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation

Content Provider	MDPI
Author	Kaiser, Susanna
Copyright Year	2020
Description	In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM.
Starting Page	464
e-ISSN	20782489
DOI	10.3390/info11100464
Journal	Information
Issue Number	10
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2020-09-30
Access Restriction	Open
Subject Keyword	Information Transportation Science and Technology Indoor Navigation Pedestrian Dead Reckoning Simultaneous Localization and Mapping Footslam Collaborative Mapping Professional Use Cases
Content Type	Text
Resource Type	Article

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