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Paper ThCT8.4

DROUILLY, Romain (INRIA Sophia-antipolis), Rives, Patrick (INRIA), Morisset, Benoit (SRI International)

Hybrid Metric-Topological-Semantic Mapping in Dynamic Environments

Scheduled for presentation during the Regular session "Mapping 2" (ThCT8), Thursday, October 1, 2015, 12:05−12:20, Saal F

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 19, 2019

Keywords Mapping, SLAM, Localization

Abstract

Mapping evolving environments requires an update mechanism to efficiently deal with dynamic objects. In this context, we propose a new approach to update maps pertaining to large-scale dynamic environments with semantics. While previous works mainly rely on large amount of observations, the proposed framework is able to build a stable representation with only two observations of the environment. To do this, scene understanding is used to detect dynamic objects and to recover the labels of the occluded parts of the scene through an inference process that takes into account both spatial context and a class occlusion model. Our method was evaluated on a database acquired at two different time-scales with an interval of three years in a large dynamic outdoor environment. It shows the ability to retrieve the hidden classes with a precision score of 0.98 and improved localisation performances.

 

 

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