Abstract
Recognizing objects is a fundamental capability for robotic systems but comparing algorithms on similar testing situations remains a challenge. This makes characterizing the current state-of-the-art difficult and impedes progress on the task. We describe a recently proposed benchmark for robotic object recognition, named the UBC Visual Robot Survey,which is a robot-collected dataset of cluttered kitchen scenes. The dataset contains imagery and range data collected from a dense sampling of viewpoints. Objects have been placed in realistic configurations that result in clutter and occlusion, similar to common home settings. This data and accompanying tools for simulation from real data enable the study of robotic recognition methods. They specifically allow focus on specific concerns in robotics such as spatial evidence integration and active perception.We describe the method used to produce the dataset in detail, a suite of testing protocols and the current state-of-the-art performance on the dataset.
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Meger, D., Little, J.J. (2013). The UBC Visual Robot Survey: A Benchmark for Robot Category Recognition. In: Desai, J., Dudek, G., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 88. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00065-7_65
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DOI: https://doi.org/10.1007/978-3-319-00065-7_65
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