Fast Hybrid Relocation in Large Scale Metric-Topologic-Semantic Map

Romain Drouilly 1, 2 Patrick Rives 1 Benoit Morisset 2
1 Lagadic - Visual servoing in robotics, computer vision, and augmented reality
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Navigation in large scale environments is challeng- ing because it requires accurate local map and global relocation ability. We present a new hybrid metric-topological-semantic map structure, called MTS-map, that allows a fine metric-based navigation and fast coarse query-based localisation. It consists of local sub-maps connected through two topological layers at metric and semantic levels. Semantic information is used to build concise local graph-based descriptions of sub-maps. We propose a robust and efficient algorithm that relies on MTS-map structure and semantic description of sub-maps to relocate very fast. We combine the discriminative power of semantics with the robustness of an interpretation tree to compare the graphs very fast and outperform state-of-the-art-techniques. The proposed approach is tested on a challenging dataset composed of more than 13000 real world images where we demonstrate the ability to relocate within 0.12ms.
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Communication dans un congrès
IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'14, Sep 2014, Chicago, United States. 2014
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Romain Drouilly, Patrick Rives, Benoit Morisset. Fast Hybrid Relocation in Large Scale Metric-Topologic-Semantic Map. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'14, Sep 2014, Chicago, United States. 2014. 〈hal-01010231〉

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