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国内首个占据栅格网络全栈课程《从BEV到Occupancy Network，算法原理与工程实践》，包含端侧部署。Surrounding Semantic Occupancy Perception Course for Autonomous Driving (docs, ppt and source code) 在线课程主页：http://111.229.117.200:8100/ (作者独立搭建)
Autoware - the world's leading open-source software project for autonomous driving
[ICRA2024] Official code of the paper "Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter"
Paper list and source code for multi-object-tracking
[NeurIPS Workshop 2019] Official code of the paper "Probabilistic 3D Multi-Object Tracking for Autonomous Driving." First Place of the First NuScenes Tracking Challenge in the AI Driving Olympics W…
Resources for Multiple Object Tracking (MOT)
A project for 3D multi-object tracking
A summary and list of open source 3D multi object tracking and datasets at this stage.
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
Open-source software for self-driving vehicles
Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.
A project demonstrating Lidar related AI solutions, including three GPU accelerated Lidar/camera DL networks (PointPillars, CenterPoint, BEVFusion) and the related libs (cuPCL, 3D SparseConvolution…
[ECCV 2022 Oral] OpenLane: Large-scale Realistic 3D Lane Dataset
[NeurIPS 2023 Track Datasets and Benchmarks] OpenLane-V2: The First Perception and Reasoning Benchmark for Road Driving
The World's First Large Scale Lidar Lane Detection Dataset and Benchmark
A list of references on lidar point cloud processing for autonomous driving
This is the short, personal project. The goal of this project is to detect the ego lane markings and conduct polynomial fitting with small LiDAR point cloud. Due to lack of data, implementing Deep …
ROS & ROS2 Implementation of Patchwork++
SOTA fast and robust ground segmentation using 3D point cloud (accepted in RA-L'21 w/ IROS'21)
Patchwork++: Fast and robust ground segmentation method for 3D LiDAR scans. @ IROS'22
点云分割论文2017 Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications
Ground Segmentation from Lidar Point Clouds
🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.
(ITSC 2021) Optimising the selection of samples for robust lidar camera calibration. This package estimates the calibration parameters from camera to lidar frame.