Depth Image-Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking.
Ping Jiang,Yoshiyuki Ishihara,Sugiyama Nobukatsu,Junji Oaki,Seiji Tokura,Atsushi Sugahara,Akihito Ogawa +6 more
TLDR
A surface feature descriptor is proposed to extract surface features (center position and normal) and refine the predicted grasp point position, removing the need for texture features for vision-guided robot control and sim-to-real modification for DCNN model training.Abstract:
Bin-picking of small parcels and other textureless planar-faced objects is a common task at warehouses. A general color image-based vision-guided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for textureless objects. Further, prior preparation of huge numbers of goal images is impractical at a warehouse. In this paper, we propose a novel depth image-based vision-guided robot bin-picking system for textureless planar-faced objects. Our method uses a deep convolutional neural network (DCNN) model that is trained on 15,000 annotated depth images synthetically generated in a physics simulator to directly predict grasp points without object segmentation. Unlike previous studies that predicted grasp points for a robot suction hand with only one vacuum cup, our DCNN also predicts optimal grasp patterns for a hand with two vacuum cups (left cup on, right cup on, or both cups on). Further, we propose a surface feature descriptor to extract surface features (center position and normal) and refine the predicted grasp point position, removing the need for texture features for vision-guided robot control and sim-to-real modification for DCNN model training. Experimental results demonstrate the efficiency of our system, namely that a robot with 7 degrees of freedom can pick randomly posed textureless boxes in a cluttered environment with a 97.5% success rate at speeds exceeding 1000 pieces per hour.read more
Citations
More filters
Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector
Р Ю Чуйков,Д А Юдин +1 more
Journal ArticleDOI
Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection
TL;DR: In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and reorientate the randomly placed objects to a specified position and pose.
Journal ArticleDOI
Object Identification for Task-Oriented Communication with Industrial Robots.
Adam Rogowski,Piotr Skrobek +1 more
TL;DR: A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed, and the core of the solution is FCD (flexible contour description) format for description of flexible templates.
Journal ArticleDOI
Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data.
TL;DR: A novel online object-packing system which can measure the dimensions of every incoming object and calculate its desired position in a given container and the experimental results show that the proposed system successfully places the incoming various shaped objects in their proper positions.
Journal ArticleDOI
Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning.
TL;DR: A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this article, where the proposed generation-evaluation framework is the core concept of the classification.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Journal ArticleDOI
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
TL;DR: Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: SSD as mentioned in this paper discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, and combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.