LucianZhong's repositories
algorithms
Algorithms in Python
apollo
An open autonomous driving platform
Awesome-LLM4AD
A curated list of awesome LLM for Autonomous Driving resources (continually updated)
Awesome-VLM-AD-ITS
This repository collects research papers of large Vision Language Models in Autonomous driving and Intelligent Transportation System. The repository will be continuously updated to track the latest update.
BEVDet
Official code base of the BEVDet series .
CameraCalibration
Fisheye or Normal Camera Intrinsic and Extrinsic Calibration. Surround Camera Bird Eye View Generator.
CenterFusion
NN-based radar-camera post sensor fusion implemented by TensorRT
GARD
Target-level monocular depth estimation tool from roadside perspectives
GraphBasedLocalTrajectoryPlanner
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.
lidar-slam-detection
LSD (LiDAR SLAM & Detection) is an open source perception architecture for autonomous vehicle/robotic
LIO-SAM-6AXIS-INTENSITY
LIO-SAM-6AXIS with intensity image loop optimization
nif
KAIST team software stack for the Indy Autonomous Challenge.
nuscenes-devkit
The devkit of the nuScenes dataset.
PLAuto
A autonomous driving framework for both Platoon and single vehicle based on ROS1.
plot
GitHub mirror of our basic C++ plotting library
raw-gnss-fusion
Code, data, and results for fusing raw GNSS data with other sensing modalities
Ros_Qt5_Gui_App
ROS human computer interface based on Qt5(基于Qt5的ROS人机交互界面)
SC-LIO-SAM
LiDAR-inertial SLAM: Scan Context + LIO-SAM
SLAM-application
LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE application and comparison on Gazebo and real-world datasets. Installation and config files are provided.
SLAM_interface
Slam-Interface is a UI interface that can interact with the ROS system, supporting users to encapsulate common ROS system operations into simple UI interface operations.
Traffic-signal-recognition--
本项目使用YOLOv4模型，并在对数字信号灯进行数字识别时采用opencv算法。
xtreme1
Xtreme1 - The Next GEN Platform for Multimodal Training Data. #3D annotation, 3D segmentation, lidar-camera fusion annotation, image annotation and rlhf tools are supported!