Integrating a low-cost mems imu into a laser-based slam for indoor mobile mapping

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Abstract

Indoor mapping techniques are highly important in many applications, such as human navigation and indoor modelling. As satellite positioning systems do not work in indoor applications, several alternative navigational sensors and methods have been used to provide accurate indoor positioning for mapping purposes, such as inertial measurement units (IMUs) and simultaneous localisation and mapping algorithms (SLAM). In this paper, we investigate the benefits that the integration of a low-cost microelectromechanical system (MEMS) IMU can bring to a feature-based SLAM algorithm. Specifically, we utilize IMU data to predict the pose of our backpack indoor mobile mapping system to improve the SLAM algorithm. The experimental results show that using the proposed IMU integration method leads into a more robust data association between the measured points and the model planes. Notably, the number of points that are assigned to the model planes is increased, and the root mean square error (RMSE) of the residuals, i.e. distances between these measured points and the model planes, is decreased significantly from 1.8 cm to 1.3 cm.
Original languageEnglish
Title of host publicationThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publication6th International Workshop LowCost 3D – Sensors, Algorithms, Applications
EditorsP. Grussenmeyer, A. Murtiyoso, H. Macher, R. Assi
Place of PublicationStrasbourg
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages149-156
Number of pages8
VolumeXLII-2/W17
DOIs
Publication statusPublished - 29 Nov 2019

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9034

Keywords

  • ITC-GOLD

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    Karam, S., Lehtola, V., & Vosselman, G. (2019). Integrating a low-cost mems imu into a laser-based slam for indoor mobile mapping. In P. Grussenmeyer, A. Murtiyoso, H. Macher, & R. Assi (Eds.), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: 6th International Workshop LowCost 3D – Sensors, Algorithms, Applications (Vol. XLII-2/W17, pp. 149-156). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences). Strasbourg: International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-archives-XLII-2-W17-149-2019