Towards Unsupervised And Incremental Semantic Mapping

Guillaume Duceux 1, 2
2 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : Robots are slowly entering our houses. But for them to perform more difficult and interesting tasks, they need to have a richer knowledge of the different objects and places present in a particular household. Building and using such a complex knowledge is called semantic mapping and navigation, which requires many different capabilities such as mapping, localization and object recognition. Most of them have been thoroughly studied in the past but often separately and in a different context. We propose a complete solution to perform incremental and unsupervised semantic mapping working on a real robot. Each aspect of the solution is discussed from the software architecture to all the functionalities needed by the robot. Some already existing techniques have been derived in the effort of integration, but more importantly three different parts of the solution are original contributions. The first one is the use of 2D laser range finder to perform object recognition using multiple views of the objects. Numerous semantic mapping solutions are using this sensor for mapping and obstacle avoidance, but we fully leveraged it by developing techniques to use it in the entire process of semantic mapping, showing that it is effective at recognizing up to 20 different objects in an indoor scenario. Secondly we introduce the graph-of-views object modeling method. In our effort to extend bag-of-views techniques to perform multi-modal modeling of objects we derived an original formulation which is more adapted to partial perception of objects. We applied this approach to object recognition using both a laser range finder and a RGB-D camera, showing that it is able to take advantage of both sensor strengths. Finally, while most existing semantic mapping techniques rely on supervised learning of objects, we present an original incremental and unsupervised learning algorithm. Our technique was applied to the learning of multi-modal models of dynamic objects that are encountered as the robot navigate an indoor environment for an extended period of time, showing that it is able to produce consistent models of these objects without human intervention.
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Guillaume Duceux. Towards Unsupervised And Incremental Semantic Mapping . Robotics [cs.RO]. ENSTA ParisTech, 2015. English. ⟨tel-01252831⟩

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