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The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies
Yuwei Chen1, Jian Tang2,3, Changhui Jiang4,5
1Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute (FGI), Geodeetinrinne 2, FI-02431 Kirkkonummi, Finland. Yuwei.chen@nls.fi.
Sensors (Basel, Switzerland)
|September 28, 2018
Summary
This study compares three indoor mapping systems: Matterport, SLAMMER, and NAVIS. SLAMMER achieved the highest accuracy (1.7 cm RMS error), offering a valuable guide for selecting Simultaneous Localization and Mapping (SLAM) hardware for indoor Location Based Services (LBS).
Area of Science:
- Geomatics and Geospatial Technology
- Robotics and Artificial Intelligence
- Indoor Positioning and Mapping
Background:
- Growing demand for indoor Location Based Services (LBS) necessitates accurate and efficient indoor mapping.
- Traditional surveying methods struggle with the complexity and dynamic nature of indoor environments.
- Simultaneous Localization and Mapping (SLAM) systems offer a promising alternative using sensors like LiDAR and depth cameras.
Purpose of the Study:
- To characterize and compare the performance of different indoor mapping systems.
- To provide guidance for selecting appropriate hardware for SLAM-based indoor mapping applications.
- To evaluate the accuracy and efficiency of commercial and research-grade mapping systems.
Main Methods:
- Comparative analysis of three indoor mapping systems: Matterport (depth cameras), SLAMMER (LiDAR, Hector-SLAM/Graph-SLAM), and NAVIS (LiDAR, IMLE algorithm).
- Testing conducted in diverse indoor environments: an L-shaped corridor and an open-style library.
- Quantitative evaluation using Terrestrial Laser Scanner (TLS) as a reference, assessing Root Mean Square (RMS) errors and feature detection rates.
Main Results:
- SLAMMER demonstrated the highest accuracy with RMS errors of 1.7 cm (MBR features) and 2.0 cm (interactive features).
- NAVIS and Matterport also achieved centimeter-level accuracy, with RMS errors ranging from 3.2 cm to 4.7 cm.
- All systems provided high feature detection rates (92.3%–100%), with variations influenced by point cloud density and quality.
Conclusions:
- All evaluated SLAM systems can generate centimeter-level indoor maps, suitable for various LBS applications.
- System selection should consider the trade-offs between cost, footprint, accuracy requirements, and point cloud quality.
- SLAMMER offers superior accuracy, while NAVIS and Matterport provide viable alternatives depending on specific project constraints.
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