Multi-sensor SLAM for Tactical Situational Awareness

Laura Ruotsalainen, Martti Kirkko-Jaakkola, Liang Chen, Simo Gröhn, Robert Guinness, Heidi Kuusniemi

Peer Reviewed

Abstract: Tactical and rescue operations need infrastructure-free accurate and reliable localization, information about the possibly unknown environment and knowledge of the status of each individual. Simultaneous Localization and Mapping (SLAM) is a key technology providing means for the first two mentioned needs. In this paper we discuss a particle filter fusion for obtaining accurate localization for use in the SLAM algorithm. At present, there is no single infrastructure-free method providing accurate and reliable positioning indoors, but the key is to adaptively integrate measurements from various sensors having different sources of error and different operational restrictions. Here we discuss the principles of obtaining motion measurements using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Then, the implementation of a particle filter integrating all measurements for an accurate and reliable localization solution for use in SLAM will be described. Finally, experimental setup and results will be discussed. The developed method is shown to provide beyond the stateof-the-art performance and is anticipated to result in a SLAM solution addressing the demanding requirements set for a tactical or rescue system providing also situational awareness.
Published in: Proceedings of the 2016 International Technical Meeting of The Institute of Navigation
January 25 - 28, 2016
Hyatt Regency Monterey
Monterey, California
Pages: 273 - 282
Cite this article: Ruotsalainen, Laura, Kirkko-Jaakkola, Martti, Chen, Liang, Gröhn, Simo, Guinness, Robert, Kuusniemi, Heidi, "Multi-sensor SLAM for Tactical Situational Awareness," Proceedings of the 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2016, pp. 273-282.
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