Abstract
Understanding uncertainty about objects and their relations in a scene is essential for action selection in robotics. We propose a novel approach for a probabilistic representation of objects and their support relations taking into account pose and shape uncertainty. Starting with a segmented point cloud a probability distribution over the object geometry is estimated, from which samples are drawn to calculate a probability distribution over support relations. To evaluate the approach, we created a new RGB-D dataset, the KIT Support Relation dataset (KIT-SR), consisting of 60 scenes annotated with pixel-wise object labels and ground-truth support relations. Furthermore, we augmented the Object Segmentation Database (OSD) with support relation annotations. We evaluated our proposed probabilistic approach against two state-of-the-art deterministic approaches and show significantly improved precision, recall, F1, and Brier scores.
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Notes
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We use the notation \(\mathtt {SUPP}(\mathtt {A}, \mathtt {B})\) for \((\mathtt {A}, \mathtt {B}) \in \mathtt {SUPP}\).
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Acknowledgment
The research leading to these results has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project Number 146371743 – TRR 89 Invasive Computing and from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No. 731761 (IMAGINE).
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Paus, F., Asfour, T. (2021). Probabilistic Representation of Objects and Their Support Relations. In: Siciliano, B., Laschi, C., Khatib, O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_45
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