nubot-nudt Goto Github PK
Name: NuBot
Type: Organization
Bio: The NuBot team was founded in 2004, focusing on robotics research at NUDT, China
Location: ChangSha China
Name: NuBot
Type: Organization
Bio: The NuBot team was founded in 2004, focusing on robotics research at NUDT, China
Location: ChangSha China
The human will use BCI to participate in the multi-robot strategy selection and control a single-robot at any time. For this purpose, a specific simulation system has been developed, which composed of: a non-invasive BCI Emotiv Epoc; a Graphical User Interface (GUI) based on QT; a multi-robot simulation environment based on Gazebo (open-source 3D robotics simulator).
Code for paper A Safe Reinforcement Learning Approach to Autonomous Navigation of Mobile Robots in Dynamic Environments
Coach for Gazebo simulation
A simulation system based on ROS and Gazebo which is specially built for multi-robot task allocation (exploration and destruction problem)
[IROS23] Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
The simulation system based on ROS and Gazebo for RoboCup Middle Size Leage
Used for robot mobile navigation tasks. Modeling the environment with a GAT network to obtain reachability near containers, using reachability as a reward function to aid navigation tasks.
Hybrid Residuals based RGBD Visual Odometry
[IROS23] InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
kernel-based attention network for point cloud compression
[TRO] Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
[RAL] Keypoint Matching for Point Cloud Registration Using Multiplex Dynamic Graph Attention Networks
Three Gazebo models: field, goal and football according to MSL rules
[RoboCup] Code for RoboCup Middle Size League Competition by NuBot team from National University of Defense Technology
A LaTeX template for Master/PhD Thesis of NUDT
Pose-Appearance-based Loop detection
Perspective Invariant Feature Tranform: a novel RGB-D local feature
[TITS] reliable dense matching based point cloud registration for autonomous driving
[Under review] SegNet4D: Effective and Efficient 4D LiDAR Semantic Segmentation in Autonomous Driving Environments
Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning
[Under Review] SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
[RoboCup-China]**机器人大赛之中型组仿真比赛程序. Simulation Software for China Robotics Competition
Single robot basic motions simulation under Gazebo
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