Abstract
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.
Highlights
There is a need for indoor mapping in many important applications, such as the mapping of hazardous sites, indoor navigation, disaster management, location-based services, and virtual reality displays
In order to test the performance of the inertial measurement units (IMUs)-simultaneous localisation and mapping algorithms (SLAM) integration, the IMU prediction-based SLAM was run on the second dataset
In order to evaluate the benefits of this integration, we compare the number of points assigned to the planes and the root mean square error (RMSE) of the residuals in two cases: SLAM with and without IMU prediction (Table 2)
Summary
There is a need for indoor mapping in many important applications, such as the mapping of hazardous sites, indoor navigation, disaster management, location-based services, and virtual reality displays. As GNSS-based systems do not work indoors, several alternative navigational methods and sensors have been used to provide accurate indoor positioning for mapping purposes, such as simultaneous localisation and mapping algorithms (SLAMs) and inertial measurement units (IMUs). SLAM has become a key technology in indoor mapping applications, and a wide variety of different SLAM algorithms have been proposed These algorithms are based on data from cameras (Henry et al, 2014), laser scanners like HectorSLAM (Kohlbrecher et al, 2011) and Gmapping (Grisetti et al, 2007), (Lehtola et al, 2016; Wen et al, 2016)) or both (Liu et al, 2010; Naikal et al, 2009).
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