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PhD Defence Samer Karam | Developing a SLAM-based Backpack Mobile Mapping System for Indoor Mapping

Developing a SLAM-based Backpack Mobile Mapping System for Indoor Mapping

Due to the COVID-19 crisis the PhD defence of Samer Karam will take place (partly) online.

The PhD defence can be followed by a live stream.

Samer Karam is a PhD student in the department of Earth Observation Science (EOS). His supervisor is prof.dr.ir. M.G. Vosselman from the Faculty of Geo-Information Science and Earth Observation (ITC).

Indoor mobile mapping is important for a wide range of applications such as indoor navigation and positioning, mapping hazardous sites, facility management, virtual tourism and interior design. State-of-the-art indoor mobile mapping systems use a combination of light detection and ranging (LIDAR) scanners, cameras and/or inertial measurement units (IMUs) mounted on movable platforms and allow for capturing 3D data of buildings’ interiors. As global navigation satellite system (GNSS) positioning does not work inside buildings, the extensively investigated simultaneous localisation and mapping (SLAM) algorithms seem to offer a suitable solution for the problem.

In this dissertation, a SLAM-based backpack mobile mapping system (ITC-Backpack) was developed for mapping buildings’ interiors. The configuration of the ITC-Backpack consists of three 2D LIDAR scanners and an IMU. The employed SLAM is planar feature-based SLAM algorithm that exploits the LIDAR scanners and the IMU to estimate the 3D pose and plane parameters.

Representing the SLAM map by planes is advantageous for multiple reasons. First, the planar features are typically large and spatially distinct and therefore distinguishable from one another. Second, they are abundant in indoor man-made environments. Third, storing planar features takes less data space than storing the captured point clouds. Finally, the representation by planar shapes is a popular format for the state-of-the-art indoor 3D reconstruction methods.

The developed SLAM in this dissertation performs loop closure detection and correction using these planar features. This enables the backpack system to recognize an already visited place and correct for the accumulated drift.

The outputs of ITC-Backpack are reconstructed 3D planes, 3D point clouds as well as a trajectory of the system’s motion in a local coordinate system. A combination of the point cloud and the trajectory represents an advantageous supplementary information for some indoor modelling problems such as semantic interpretation and space partitioning.

The ITC-Backpack system is validated on various indoor environments that differ in terms of geometry, architecture and clutter. Moreover, we evaluate the performance of the system by comparing the obtained point clouds against those obtained from a commercial indoor mobile mapping system, Viametris[1]  iMS3D, and ground truth obtained from a terrestrial laser scanner (TLS).

[1] www.viametris.com