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Article

Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation

Institute for Communications and Navigation, German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany
Information 2020, 11(10), 464; https://doi.org/10.3390/info11100464
Received: 21 August 2020 / Revised: 27 September 2020 / Accepted: 28 September 2020 / Published: 30 September 2020
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
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. View Full-Text
Keywords: indoor navigation; pedestrian dead reckoning; simultaneous localization and mapping; FootSLAM; collaborative mapping; professional use cases indoor navigation; pedestrian dead reckoning; simultaneous localization and mapping; FootSLAM; collaborative mapping; professional use cases
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MDPI and ACS Style

Kaiser, S. Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation. Information 2020, 11, 464. https://doi.org/10.3390/info11100464

AMA Style

Kaiser S. Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation. Information. 2020; 11(10):464. https://doi.org/10.3390/info11100464

Chicago/Turabian Style

Kaiser, Susanna. 2020. "Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation" Information 11, no. 10: 464. https://doi.org/10.3390/info11100464

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