Gr2To

From HySeLAM Hybrid Semantic Localization and Mapping
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Cooperation with humans is a requirement for the next generation of robots so it is necessary to model how robots can sense, know, share and acquire knowledge from human interaction. Instead of traditional SLAM (Simultaneous Localization and Mapping) methods, which do not interpret sensor information other than at the geometric level, these capabilities require an environment map representation similar to the human representation. Topological maps are one option to translate these geometric maps into a more abstract representation of the the world and to make the robot knowledge closer to the human perception. In this paper is presented a novel approach to translate 3D grid map into a topological map. This approach was optimized to obtain similar results to those obtained when the task is performed by a human. Also, a novel feature of this approach is the augmentation of topological map with features such as walls and doors.



Gr2To Video Explanation

This is a demo of Gr2To!


Intermediate place segmentation using Gr2To in FEUP building at Floor Level of I Building
Intermediate Door Detection using Gr2To in FEUP building at Floor Level of I Building
Intermediate Door Detection using Gr2To in FEUP building at Floor Level of I Building


Intermediate place segmentation using Gr2To using an gridmap from traclabs.com
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