Visualize KITTI data. Implement YOLOv5 and SGBM algorithms in ROS2 node.

mono / stereo depth estimation + yolo object detection with deployment on Jeston nano / TX2 / GeForce by TensorRT

constrained visual servoing control for Baxter arm

Developed controllers for Franka Robot such as: force controller, hybrid force-position controller

SEIKO Controller: Multi-Contact Whole Body Force Control for Position-Controlled Robots

Developed and simulated a SCARA robot model in MATLAB. Conducted in-depth research and implemented forward and inverse kinematics, as well as trajectory and path planning for the designed robot.

软件工程常用文档模板及示例：可行性分析报告、开发计划、需求分析文档、概要设计文档、详细设计文档、用户操作手册、测试计划、测试分析报告、开发进度报告、项目开发总结报告、软件维护手册等

Implemenation of the Unscented Particle Filter according to the paper by Van Der Merwe and al.

a little demo in the matlab environment about different kinds of unlinear Kalman filters

C++ version multiple object trackers

Real-time C++ ECO tracker etc. speed-up by SSE/NEON, support Linux, Mac, Jetson TX1/2, raspberry pi

A simple visual tracking interface using Python

Header-only C++11 Cubature Kalman Filtering (CKF) implementation based on Eigen3

A fork of ROS driver for Blueprint Subsea's Oculus multibeam sonars

ROS wrapper for the Oculus M750d Multibeam Echosounder used in the Maritime Robotics Laboratory at KTH.

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

Extremely simple yet powerful header-only C++ plotting library built on the popular matplotlib

RBF network implementation as a part of Technical Neural Networks course at University of Bonn

Matlab v1.0 implementation for AutoTrack

mosse_tracker

Implementation of MOSSE tracker in MATLAB: Visual Object Tracking using Adaptive Correlation Filters (CVPR 2010)

C++ Implementation of KCF Tracker

EasySerial A cross-platform open source serial port debugging assistant based on QT

华科硕士论文word模板

Deep Reinforcement Learning (RL) algorithms for underwater target tracking with Autonomous Underwater Vehicles (AUV)

Optimally controlling AUVs in the presence of obstacles with reinforcement learning.

Path planning using Hybrid A*/RRT + Dubins Path (as final shot).

Diversity is All You Need: Learning Skills without a Reward Function in PyTorch.