FCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
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This is the slide used for IEEE International Conference on Robotics and Automation (ICRA) 2017, Workshop on Learning and Control for Autonomous Manipulation Systems on June 2nd, 2017.
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report
reportShikhar Gupta This paper presents a vision-based method for autonomous landing of a quadcopter UAV. An onboard camera and NVIDIA Jetson TK1 processor are used to detect the landing platform using SIFT feature extraction and match keypoints between the detected image and a training image. The distance and position of the landing platform relative to the UAV is then estimated. This information is used by the ROS framework to generate control signals to maneuver the UAV's roll and pitch and autonomously land it precisely on the target platform. Experiments show the method can achieve precise landing within 10 cm under steady wind conditions.
Strategy for Foreground Movement Identification Adaptive to Background Variat...
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Chen-Ping Yu, Co-founder and CEO of Phiar, presents the "Separable Convolutions for Efficient Implementation of CNNs and Other Vision Algorithms" tutorial at the May 2019 Embedded Vision Summit.
Separable convolutions are an important technique for implementing efficient convolutional neural networks (CNNs), made popular by MobileNet’s use of depthwise separable convolutions. But separable convolutions are not a new concept, and their utility is not limited to CNNs. Separable convolutions have been widely studied and employed in classical computer vision algorithms as well, in order to reduce computation demands.
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Pontillo Semanti Code Using Content Similarity And Database Driven Matching T...
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We describe a system that segments POR videos into fixations and allows users to train a database-driven, object-recognition system. A correctly trained library results in a very accurate and semi-automated translation of raw POR data into a sequence of objects, regions or materials.
Portfolio
PortfolioIvan Khomyakov Ivan Khomyakov's portfolio summarizes his skills and experience. He has extensive knowledge of programming languages like C++, C#, Python, and technologies including OpenCV, SQL, machine learning, AWS, and Unity 3D. Some of his projects include developing a fast cubemap filter for rendering environments, a real-time locating system for tracking objects, and a dynamic map module for navigation systems. He also has experience with route editing tools, augmented reality applications, medical image segmentation, and machine learning algorithms. His background includes both academic and professional work on computer vision, image processing, statistics, and more.
IRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
A Novel Background Subtraction Algorithm for Dynamic Texture Scenes
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How to Make Hand Detector on Native Activity with OpenCV
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K-Means Clustering in Moving Objects Extraction with Selective Background
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A Three-Dimensional Representation method for Noisy Point Clouds based on Gro...
A Three-Dimensional Representation method for Noisy Point Clouds based on Gro...Sergio Orts-Escolano
"Separable Convolutions for Efficient Implementation of CNNs and Other Vision...
"Separable Convolutions for Efficient Implementation of CNNs and Other Vision...Edge AI and Vision Alliance
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FCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
- 1. Hitoshi Kusano*, Ayaka Kume+, Eiichi Matsumoto+, Jethro Tan+ June 2, 2017 *Kyoto University +Preferred Networks, Inc. FCN-Based 6D Robotic Grasping for Arbitrary Placed Objects ※This work is the output of Preferred Networks internship program
- 2. Requirement for successful robotic grasping: Derive configurations of a robot and its end-effector e.g. Grasp pose, Grasp width, Grasp height, Joint angle ・Traditional approach decomposes grasping process into several stages, which require many heuristics ・Machine learning based end-to-end approach has emerged Background http://www.schunk-modular-robotics.com/ 1/9 Complex end-effector Cluttered environment
- 3. None of prior methods can predict 6D grasp Previous Work ~ Machine learning based end-to-end approach ~ Pinto2016 Levine2016 Araki2016 Guo2017 (x, y)height width 2/9 (x, y, z, roll, pitch, yaw)
- 4. Our purpose: End-to-End learning to grasp arbitrary placed objects Contribution: ○ Novel data collection strategy to obtain 6D grasp configurations using a teach tool by human ○ End-to-end CNN model predicting 6D grasp configurations Purpose and Contribution (x, y, z, w, p, r) 3/9
- 5. ● An extension for Fully Convolutional Networks ● Outputs two maps with scores: Location Map for graspability per pixel, and Configuration Map providing end-effector configurations (z, w, p, r) per pixel ● For Configuration Map, this network classifies valid grasp configurations to 300 classes, NOT regression Grasp Configuration Network (x, y, z, w, p, r) 4/9 Location MapConfiguration Map
- 6. Data Collection Simple teach tool Data Collection We demonstrated 11320 grasps for 7 objects 5/9 Robotic Gripper https://www.thk.com X
- 7. A. Intel Realsense SR300 RGB-D camera B. Arbitrary placed object C. THK TRX-S 3-finger gripper D. FANUC M-10iA 6 DOF robot arm Experiment Setup B C D A 6/9
- 8. ● Predicted grasp configurations for the same (X,Y) location Example of predicted grasp configurations Cap Bottle TOP VIEW FRONT VIEW Grasp Candidate Grasp Candidate 7/9
- 9. Known Objects Unknown Objects Results of robotic experiment 70% 50% 60% 40% 20% 40% 60% Number under the figure means success rate for 10 trials 60% 20% 20% 40% 30% 8/9 _
- 10. System Test ※This video is double speed 9/9
- 11. Thank you for listening and I hope to talk to you in the interactive session