ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 223–230, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-223-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 223–230, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-223-2020

  03 Aug 2020

03 Aug 2020

STRATEGIES TO INTEGRATE IMU AND LIDAR SLAM FOR INDOOR MAPPING

S. Karam, V. Lehtola, and G. Vosselman S. Karam et al.
  • Dept. of Earth Observation Science, Faculty ITC, University of Twente, 7514 AE Enschede, The Netherlands

Keywords: Indoor Mapping, SLAM, IMU, Mobile Laser Scanning, LiDAR, 6DOF Pose Estimation, Point Clouds

Abstract. In recent years, the importance of indoor mapping increased in a wide range of applications, such as facility management and mapping hazardous sites. The essential technique behind indoor mapping is simultaneous localization and mapping (SLAM) because SLAM offers suitable positioning estimates in environments where satellite positioning is not available. State-of-the-art indoor mobile mapping systems employ Visual-based SLAM or LiDAR-based SLAM. However, Visual-based SLAM is sensitive to textureless environments and, similarly, LiDAR-based SLAM is sensitive to a number of pose configurations where the geometry of laser observations is not strong enough to reliably estimate the six-degree-of-freedom (6DOF) pose of the system. In this paper, we present different strategies that utilize the benefits of the inertial measurement unit (IMU) in the pose estimation and support LiDAR-based SLAM in overcoming these problems. The proposed strategies have been implemented and tested using different datasets and our experimental results demonstrate that the proposed methods do indeed overcome these problems. We conclude that IMU observations increase the robustness of SLAM, which is expected, but also that the best reconstruction accuracy is obtained not with a blind use of all observations but by filtering the measurements with a proposed reliability measure. To this end, our results show promising improvements in reconstruction accuracy.