3D ActionSLAM: wearable person tracking in multi-floor environments

We present 3D ActionSLAM, a stand-alone wearable system that can track people in previously unknown multi-floor environments with sub-room accuracy. ActionSLAM stands for action-based simultaneous localization and mapping: It fuses dead reckoning data from a foot-mounted inertial measurement unit wi...

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Bibliographic Details
Published in:Personal and ubiquitous computing Vol. 19; no. 1; pp. 123 - 141
Main Authors: Hardegger, Michael, Roggen, Daniel, Tröster, Gerhard
Format: Journal Article
Language:English
Published: London Springer London 01.01.2015
Springer Nature B.V
Subjects:
Algorithms
Computation
Computer Science
Construction
Errors
Global positioning systems
GPS
Landmarks
Mapping
Mobile Computing
Original Article
Pedestrians
Personal Computing
Three dimensional
Three dimensional imaging
Tracking (position)
User Interfaces and Human Computer Interaction
Walking
Wearable
Wearable systems
Simultaneous localization and mapping
Location-aware computing
Indoor tracking
Pedestrian dead reckoning
ISSN:1617-4909, 1617-4917
Online Access:Get full text
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Summary:We present 3D ActionSLAM, a stand-alone wearable system that can track people in previously unknown multi-floor environments with sub-room accuracy. ActionSLAM stands for action-based simultaneous localization and mapping: It fuses dead reckoning data from a foot-mounted inertial measurement unit with the recognition of location-related actions to build and update a local landmark map. Simultaneously, this map compensates for position drift errors that accumulate in open-loop tracking by means of a particle filter. To evaluate the system performance, we analyzed 23 tracks with a total walked distance of 6,489 m in buildings with up to three floors. The algorithm robustly (93 % of runs converged) mapped the areas with a mean landmark positioning error of 0.59 m. As ActionSLAM is fully stand-alone and not dependent on external infrastructure, it is well suited for patient tracking in remote health care applications. The algorithm is computationally light-weight and runs in real-time on a Samsung Galaxy S4, enabling immediate location-aware feedback. Finally, we propose visualization techniques to facilitate the interpretation of tracking data acquired with 3D ActionSLAM.
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ISSN:1617-4909
1617-4917
DOI:10.1007/s00779-014-0815-y