14,814 research outputs found
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Grasping skill is a major ability that a wide number of real-life
applications require for robotisation. State-of-the-art robotic grasping
methods perform prediction of object grasp locations based on deep neural
networks. However, such networks require huge amount of labeled data for
training making this approach often impracticable in robotics. In this paper,
we propose a method to generate a large scale synthetic dataset with ground
truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
images and annotations of successful grasping positions based on grasp attempts
performed in a simulated environment. We carried out experiments using an
off-the-shelf CNN, with three different evaluation metrics, including real
grasping robot trials. The results show that Jacquard enables much better
generalization skills than a human labeled dataset thanks to its diversity of
objects and grasping positions. For the purpose of reproducible research in
robotics, we are releasing along with the Jacquard dataset a web interface for
researchers to evaluate the successfulness of their grasping position
detections using our dataset
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