Dean's repositories
PythonRobotics
Python sample codes for robotics algorithms.
YOLOv8-DeepSORT-Object-Tracking
YOLOv8 Object Tracking using PyTorch, OpenCV and DeepSORT
Euro-Truck-Simulator-2-Lane-Assist
Plugin based interface program for ETS2/ATS.
iros20-6d-pose-tracking
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
open3d_slam
Pointcloud-based graph SLAM written in C++ using open3D library.
Person_reID_baseline_pytorch
Pytorch ReID: A tiny, friendly, strong pytorch implement of object re-identification baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
ros_motion_planning
Motion planning and Navigation of AGV/AMR:ROS planner plugin implementation of A*(A Star), JPS(Jump Point Search), D*(D Star), LPA*, D* Lite, RRT, RRT*, RRT-Connect, Informed RRT*, PID, DWA etc.
act-plus-plus
Imitation Learning algorithms with Co-traing for Mobile ALOHA: ACT, Diffusion Policy, VINN
bot_box
Universal remote control for robots. Works via the internet.
BundleSDF
[CVPR 2023] BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
Depth-Anything
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
laser_assembler
Provides nodes to assemble point clouds from either LaserScan or PointCloud messages
learnopencv
Learn OpenCV : C++ and Python Examples
nmea_navsat_driver
ROS package containing drivers for NMEA devices that can output satellite navigation data (e.g. GPS or GLONASS).
opentcs
The open Transportation Control System (by Fraunhofer IML)
rmf_demos
Demonstrations of the OpenRMF software
rmf_ros2
Internal ROS infrastructure for RMF
slam_toolbox
Slam Toolbox for lifelong mapping and localization in potentially massive maps with ROS
Ultra-Fast-Lane-Detection-v2
Ultra Fast Deep Lane Detection With Hybrid Anchor Driven Ordinal Classification (TPAMI 2022)
Vehicle-CV-ADAS
The project can achieve FCWS, LDWS, and LKAS functions solely using only visual sensors. using YOLOv5 / YOLOv5-lite / YOLOv8 and Ultra-Fast-Lane-Detection-v2 .