Robotic perception of object properties using tactile sensing

Jiang, Jianrong; Luo, Shan

doi:10.1016/b978-0-32-390445-2.00009-x

Cited by 2 publications

(1 citation statement)

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“…Table 1 summarizes recent datasets for transparent object recognition. As mainstream, most researchers were focused on the RGB-based datasets (Bashkirova et al, 2022;Chen et al, 2018Chen et al, , 2022Dai et al, 2022;Fang et al, 2022;Jiang et al, 2022;Jiang and Shan Ll, 2022b;Lin et al, 2021;Liu et al, 2020Liu et al, , 2021Mei et al, 2020;Proença and Simoes, 2020;Sajjan et al, 2020;Xie et al, 2020;Xu et al, 2022). These datasets have necessitated background clutters using additional artifacts to overcome the attribute of the transparent objects heavily dependent on the background.…”

Section: Related Workmentioning

confidence: 99%

TRansPose: Large-scale multispectral dataset for transparent object

Kim,

Jeon,

Jung

et al. 2023

The International Journal of Robotics Research

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Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using an FLIR A65 thermal infrared camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. Supplementary material can be accessed from the following link: https://sites.google.com/view/transpose-dataset .

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