Autonomous Vehicles
Showing 36 of 83 projects
A TensorFlow implementation for generating semantically segmented bird's eye view images from multiple vehicle-mounted cameras using a Sim2Real deep learning approach.
A Siamese neural network for LiDAR-based loop closing and localization by predicting scan overlap and relative yaw angle.
ROS packages for interfacing with Velodyne 3D LIDAR sensors in robotics applications.
An open-source machine learning system for training autonomous RC cars using computer vision and neural networks.
A CUDA-accelerated library collection for point cloud processing, providing GPU-optimized alternatives to PCL functions.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A ROS package for calibrating camera and LiDAR sensors using OpenCV's PnP and Levenberg-Marquardt optimization.
A curated list of free software and open hardware for building remote-controlled multicopters, airplanes, and flying wings.
A LiDAR-based tool for constructing static maps by removing dynamic points from point cloud sequences.
A Rust-based solver for fast, embedded nonconvex parametric optimization with code generation and ROS support.
A ROS/ROS2 bridge enabling two-way communication between the CARLA autonomous driving simulator and ROS ecosystems.
Pure pursuit controller and Reeds-Shepp sampling-based planner for car-like vehicle navigation in SE(2) space.
A fast and robust ground segmentation algorithm for 3D LiDAR point clouds, using concentric zone-based region-wise processing.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A ROS and Gazebo simulation of a Toyota Prius with sensor data publishing and gamepad/joystick control.
A ROS-based calibration tool for estimating extrinsic poses of lidar, radar, and camera sensor setups.
A modular autonomous driving platform for developing and testing AV components on CARLA simulator and real-world vehicles.
A ROS driver for Livox LiDAR devices (Mid-40, Mid-70, Tele-15, Horizon, Avia) to publish point cloud data.
A Python implementation for fully automatic extrinsic calibration of 3D LiDAR and cameras using laser reflectance intensity.
A ROS library for LiDAR point cloud segmentation, enabling ground removal and object clustering for autonomous vehicle perception.
Open source software and hardware for vehicle control, stabilization, autonomous vehicles, and robotics.
A real-time ROS 2 package for detecting drivable roads and sidewalks from LIDAR point clouds in urban autonomous driving scenarios.
A C++ library implementing the Responsibility Sensitive Safety (RSS) model for autonomous vehicle decision-making.
A full LiDAR SLAM system for static environment mapping using LiDAR with optional GPS, IMU, and odometry support.
A ROS-based dataset and tools for autonomous vehicle development with seasonal multi-sensor data from Ford vehicles.
A full implementation of the OMG Data Distribution Service (DDS) standard for real-time data sharing in distributed systems.
A C++ library implementing steering functions for car-like robots with limited turning radius, including Dubins, Reeds-Shepp, and continuous curvature variants.
A ROS package extension for ORB-SLAM2 that enables saving and loading ORB feature maps for closed-circuit visual localization of autonomous vehicles.
A desktop tool for viewing, editing, and saving road network maps for autonomous vehicle platforms like Autoware.
A self-supervised deep learning model for extrinsic calibration between LiDAR and camera sensors using 3D spatial transformer networks.
A curated collection of LiDAR place recognition methods, datasets, and algorithms for robotics and autonomous systems.
A simulation-based deep learning approach to enhance the resolution of 3D lidar point clouds for ground vehicles.
A ROS 2 package providing waypoint management and path-following control nodes for autonomous vehicles and robots.
An open-source library of safety processes and testing procedures for self-driving car startups to supplement their safety programs.
A multi-sensor dataset for autonomous vehicle and robot navigation, featuring synchronized camera, LiDAR, IMU, and GNSS data collected in urban environments.
A deep learning approach that unifies global place recognition and local 6DoF pose refinement for robust relocalization in large-scale 3D point clouds.
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