Nothing Special   »   [go: up one dir, main page]

Samarawickrama, 2021 - Google Patents

RGB-D Based Deep Learning Methods for Robotic Perception and Grasping

Samarawickrama, 2021

View PDF
Document ID
11660481834449135145
Author
Samarawickrama K
Publication year

External Links

Snippet

The recent advancements in robotic perception have vested robotic grasping with learning capabilities. During the past decade, empirical methods on grasp detection have been preempted by the data-driven methods highlighting the potential of deep learning and …
Continue reading at trepo.tuni.fi (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244Information retrieval; Database structures therefor; File system structures therefor in image databases
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis

Similar Documents

Publication Publication Date Title
Newbury et al. Deep learning approaches to grasp synthesis: A review
Fang et al. Graspnet-1billion: A large-scale benchmark for general object grasping
US20210390653A1 (en) Learning robotic tasks using one or more neural networks
Turpin et al. Grasp’d: Differentiable contact-rich grasp synthesis for multi-fingered hands
Wang et al. Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation
Eppner et al. A billion ways to grasp: An evaluation of grasp sampling schemes on a dense, physics-based grasp data set
Zapata-Impata et al. Fast geometry-based computation of grasping points on three-dimensional point clouds
Patten et al. Dgcm-net: dense geometrical correspondence matching network for incremental experience-based robotic grasping
Dong et al. A review of robotic grasp detection technology
Kushwaha et al. Generating quality grasp rectangle using Pix2Pix GAN for intelligent robot grasping
Zhong et al. 3d implicit transporter for temporally consistent keypoint discovery
Veres et al. An integrated simulator and dataset that combines grasping and vision for deep learning
Le et al. Robot arm grasping using learning-based template matching and self-rotation learning network
Hu et al. Automated BIM-to-scan point cloud semantic segmentation using a domain adaptation network with hybrid attention and whitening (DawNet)
Samarawickrama RGB-D Based Deep Learning Methods for Robotic Perception and Grasping
ten Pas et al. Efficient and accurate candidate generation for grasp pose detection in se (3)
Welle et al. Partial caging: a clearance-based definition, datasets, and deep learning
Sefat et al. SingleDemoGrasp: Learning to Grasp From a Single Image Demonstration
Martínez-Franco et al. Machine Vision for Collaborative Robotics Using Synthetic Data-Driven Learning
Kumar et al. High-speed detector for low-powered devices in aerial grasping
Le Relationship between Grasping Actions and Object Attributes: A Survey [J]
Ahmad Robotic assembly, using RGBD-based object pose estimation & grasp detection.
Schneider et al. Synthetic Data Generation on Dynamic Industrial Environment for Object Detection, Tracking, and Segmentation CNNs
Niu et al. Customizable 6 degrees of freedom grasping dataset and an interactive training method for graph convolutional network
Yang et al. Research on robot classifiable grasp detection method based on convolutional neural network