Lidar
Showing 36 of 74 projects
A Python package for visualizing and processing 2D/3D point clouds with interactive rendering and parallelized queries.
A toolkit and dataset for autonomous driving research, including trajectory prediction, 3D LiDAR detection, scene parsing, and video inpainting.
A fast and robust ground segmentation algorithm for 3D LiDAR point clouds, using concentric zone-based region-wise processing.
A low-cost and accurate SLAM system that fuses Livox lidar with camera data for robust localization and mapping.
A benchmark dataset for long-range (up to 250m) dense depth estimation in autonomous driving, featuring 360° LiDAR ground truth.
A ROS-based calibration tool for estimating extrinsic poses of lidar, radar, and camera sensor setups.
A simple, robust, and accurate 3D LiDAR SLAM system designed to just work.
A robust system for multi-LiDAR extrinsic calibration, real-time odometry, and mapping without manual intervention.
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 real-time, uncertainty-aware deep learning model for semantic segmentation of 3D LiDAR point clouds in autonomous driving.
ROS & ROS2 implementation of Patchwork++, a fast and robust ground segmentation method for 3D LiDAR point clouds.
A ROS 2 package for tightly-coupled LiDAR-inertial SLAM using NDT/GICP scan matching with loop closure.
A lightweight neural network for near-real-time semantic segmentation of LiDAR point clouds using polar coordinate quantization.
A real-time ROS 2 package for detecting drivable roads and sidewalks from LIDAR point clouds in urban autonomous driving scenarios.
A full LiDAR SLAM system for static environment mapping using LiDAR with optional GPS, IMU, and odometry support.
Real-time visualization and processing tool for live 3D LiDAR data from Velodyne sensors.
A ROS-based dataset and tools for autonomous vehicle development with seasonal multi-sensor data from Ford vehicles.
Real-time reception, recording, visualization, and processing of 3D LiDAR data from multiple manufacturers.
A curated list of top-tier publications and resources for LiDAR-Visual fusion SLAM systems.
A real-time fiducial tag system for LiDAR point clouds, robust to lighting and compatible with visual markers like AprilTags.
A Python devkit for loading, exploring, and manipulating the PandaSet, a large-scale autonomous driving dataset with LiDAR, camera, and annotations.
A long-term autonomous driving dataset from Europe with multi-sensor data (GPS-RTK, LiDAR, cameras, IMU) for localization and mapping research.
A recursive B-spline-based state estimation framework for 6-DoF LiDAR odometry, supporting LiDAR-only, LiDAR-inertial, and multi-LiDAR configurations.
A large-scale driving behavior dataset with LiDAR point clouds, dashboard videos, and sensor data for autonomous driving research.
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.
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
A ROS library for robust plane segmentation from LIDAR, depth camera data, and elevation maps using normal-based clustering.
A multi-sensor dataset for autonomous vehicle and robot navigation, featuring synchronized camera, LiDAR, IMU, and GNSS data collected in urban environments.
A robust, low-drift, real-time SLAM package for the Livox Horizon LiDAR, designed for highway autonomous driving scenarios.
A lightweight, cross-platform, single-header C++ point cloud viewer for visualizing LiDAR, photogrammetry, and 3D datasets.
A Python devkit for working with the Boreas and Boreas Road Trip all-weather autonomous driving datasets.
A 3D object detection method that exploits visibility information from LiDAR point clouds to improve accuracy.
A ROS 2 node for real-time LiDAR ground segmentation using a two-phase grid-based algorithm for robotic perception.
A graphical user interface for annotating point clouds and 3D scenes with bounding boxes, keypoints, and rectangles.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.