Name: Sungwook Cho
Type: User
Company: Cheongju University
Bio: Ph.D. in Aerospace Engineering,
Assistant Professor,
Major of Aeronautical and Mechanical Engineering, Division of Aeronautics,
Cheongju University
Location: Cheongju, Chungbuk, South Korea
Sungwook Cho's Projects
케라스 창시자에게 배우는 딥러닝
AR.Drone package for ROS Kinetic.
Robot arm control based on ros-control framework
Use AWS RoboMaker and demonstrate running a simulation which trains a reinforcement learning (RL) model to drive a car around a track
A MATLAB GUI for drawing asymptotic Bode diagrams
Caffe implementation of SSD detection network,such as Google MobileNet and SqueezeNet
image logger using camera (monocular/stereo camera)
cpp implementation of robotics algorithms including localization, mapping, SLAM, path planning and control
YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Repo for the Deep Learning Nanodegree Foundations program.
《정석으로 배우는 딥러닝: 텐서플로와 케라스로 배우는 시계열 데이터 처리 알고리즘》 예제 코드
Image Database Generator for CNN
Dubins Path Planner C++ Library
Study for Dual Quaternion-based Dynamic System
청주대학교 융합신기술대학원 기계항공산업학과 2021년도 1학기 임베디드시스템 실습자료
Semantic segmentation for sidewalks. It has different models and options to train over.
Simultaneous localization and mapping using fiducial markers.
Example robots and code for interfacing Gazebo with ROS
핸즈온 머신러닝 2/E의 주피터 노트북
Mask R-CNN, FPN, LinkNet, PSPNet and UNet with multiple backbone architectures support readily available
Driver for Lord Corporation Microstrain 3DM GX4 25
ROS packages for the InterbotiX X-series family of robotic arms and turrets
ROS test class
An open source flight dynamics & control software library
🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
An implementation of real-time optimal trajectory generation bases on the minimum snap trajectory.
Simulator for the MBZIRC Maritime Grand Challenge
Quadrotor control using minimum snap trajectory optimization and SE3 geometric controller
Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.