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Enhancing collaborative SLAM with semantic information for human-readable mapping
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Sähkötekniikan korkeakoulu |
Master's thesis
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ELEC3055
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en
Pages
57
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Abstract
This thesis discusses the enhancement of Collaborative Simultaneous Localization and Mapping (C-SLAM) system based on visual data with semantic information to produce human-readable maps. This work covers three steps of the semantic information integration: acquisition, aggregation and post-processing. By applying a state-of-the-art semantic segmentation neural network on each robot's video stream, the system is capable of obtaining the semantic information from multiple sources and classifying all the 3D map points with relevant semantic labels. These points are then used to extract a floorplan of the environment.
The system was tested in a real office environment using three handheld devices. The experiment showed that the system can build a semantic point cloud and accurately extract wall outlines, room clusters, and scene objects. The findings suggest that semantic integration into C-SLAM systems can bridge the gap between human and robotic understanding of mapped environments.