Showing 21-40 of 45 projects
Lightweight, tightly coupled lidar-inertial odometry using parallel sparse incremental voxels.
A versatile and extensible 3D localization and mapping framework for point cloud-based applications.
A comprehensive guide to the powerful photogrammetry technique used for 3D reconstruction, surveying, and more.
A Python script that automatically updates daily Computer Vision papers from the ArXiv using GitHub Actions.
lidar-based localization and mapping library for robotics and autonomous vehicles
OpenDriveLab's Birds-eye-view Perception project provides a cookbook and research for autonomous driving, with Python implementation.
A C++ toolbox for target-less LiDAR-camera calibration, supporting ROS1 and ROS2 environments.
A PyTorch implementation of the Complex-YOLO 3D object detection algorithm for LiDAR point clouds.
A robust, real-time LiDAR-inertial initialization method for robotics and SLAM applications.
Fast and robust clustering of point clouds generated with a Velodyne sensor, useful for robotics and autonomous driving applications.
An awesome list of LIDAR hardware, software, datasets, and tools for autonomous driving and 3D applications.
Automatically calibrates high-resolution LiDAR and camera in targetless scenes.
A collection of papers, toolboxes, and notes related to LiDAR-Camera calibration and fusion.
An all-in-one data labeling and annotation platform for multimodal data training, supporting 3D LiDAR, images, and language models.
A Jupyter Notebook for visualizing 3D object detection and LiDAR point clouds from the KITTI dataset.
A fast and optimized LIDAR-based localization and mapping library for indoor/outdoor robotics applications.
A C++ library for calibrating LiDAR-IMU systems without using special targets.
Point-LIO is a C++ library for robust and accurate LiDAR-Inertial Odometry, a key technology for autonomous navigation.
Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds
A comprehensive collection of SLAM (Simultaneous Localization and Mapping) applications and comparisons for robotics and lidar-based navigation.
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