Title: Simultaneous Localization and Mapping using Terrestrial Multipath Signals, GNSS and Inertial Sensors
Author(s): Christian Gentner, Robert Poehlmann, Markus Ulmschneider, Thomas Jost, and Armin Dammann
Published in: Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
Portland, Oregon
Pages: 2981 - 2993
Cite this article: Gentner, Christian, Poehlmann, Robert, Ulmschneider, Markus, Jost, Thomas, Dammann, Armin, "Simultaneous Localization and Mapping using Terrestrial Multipath Signals, GNSS and Inertial Sensors," Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2981-2993.
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Abstract: This paper develops a multisensor positioning filter for seamless indoor and outdoor positioning. Generally, multipath propagation of radio signals degrades the accuracy of the positioning device if the receiver is based on standard methods. In contrast, we use a multipath assisted positioning algorithm called Channel-SLAM, which exploits multipath propagation of radio signals emitted by a terrestrial transmitter for positioning. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) with static location. Hence, with each received MPC the number of transmitters increases. As the locations of these VTs are unknown, Channel-SLAM concurrently estimates the position of the user and the VTs using a simultaneous localization and mapping (SLAM) methodology. This enables positioning with only a single physical transmitter. However, Channel-SLAM is a relative positioning system and works in a local coordinate system, which is a limitation for practical applications. Therefore this paper develops a sensor fusion concept to integrate a global navigation satellite system (GNSS) and inertial sensors to allow unique, global positioning with enhanced accuracy. The positioning algorithm is based on a Rao-Blackwellised particle filter (PF). Moreover, the developed positioning algorithm is evaluated based on measurement data obtained in a real-world outdoor and indoor scenario, where the position of the terrestrial transmitter is unknown.