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Détection et estimation de pose d'instances d'objet rigide pour la manipulation robotisée

Abstract : Visual object detection and estimation of their poses -- i.e. position and orientation for a rigid object -- is of utmost interest for automatic scene understanding.In this thesis, we address this topic through the bin-picking scenario, in which instances of a rigid object have to be automatically detected and localized in bulk, so as to be manipulated by a robot for various industrial tasks such as machine feeding, assembling, packing, etc.To this aim, we propose a novel method for object detection and pose estimation given an input depth image, based on the aggregation of local predictions through an Hough forest technique, that is suitable with industrial constraints of performance and ease of use.Overcoming limitations of existing approaches that assume objects not to have any proper symmetries, we develop a theoretical and practical framework enabling us to consider any physical rigid object, thanks to a novel definition of the notion of pose and an associated distance.This framework provides tools to deal with poses efficiently for operations such as pose averaging or neighborhood queries, and is based on rigorous mathematical developments.Evaluation benchmarks used in the literature are not very representative of our application scenario and suffer from some intrinsic limitations, therefore we formalize a methodology suited for scenes in which many object instances, partially occluded, in arbitrary poses may be considered. We apply this methodology on real and synthetic data, and demonstrate the soundness of our approach compared to the state of the art.
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Contributor : Romain Brégier Connect in order to contact the contributor
Submitted on : Friday, January 11, 2019 - 3:11:32 PM
Last modification on : Wednesday, July 6, 2022 - 4:23:44 AM
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  • HAL Id : tel-01977050, version 1



Romain Brégier. Détection et estimation de pose d'instances d'objet rigide pour la manipulation robotisée. Vision par ordinateur et reconnaissance de formes [cs.CV]. Université Grenoble Alpes, 2018. Français. ⟨NNT : 2018GREAM039⟩. ⟨tel-01977050⟩



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