784 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,
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Accurate object segmentation is a crucial task in the context of robotic
manipulation. However, creating sufficient annotated training data for neural
networks is particularly time consuming and often requires manual labeling. To
this end, we propose a simple, yet robust solution for learning to segment
unknown objects grasped by a robot. Specifically, we exploit motion and
temporal cues in RGB video sequences. Using optical flow estimation we first
learn to predict segmentation masks of our given manipulator. Then, these
annotations are used in combination with motion cues to automatically
distinguish between background, manipulator and unknown, grasped object. In
contrast to existing systems our approach is fully self-supervised and
independent of precise camera calibration, 3D models or potentially imperfect
depth data. We perform a thorough comparison with alternative baselines and
approaches from literature. The object masks and views are shown to be suitable
training data for segmentation networks that generalize to novel environments
and also allow for watertight 3D reconstruction.Comment: 15 pages, 11 figures. Video:
https://www.youtube.com/watch?v=srEwuuIIgz
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