There are 5 repositories under extrinsic-calibration topic.
Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.
This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
Azure Kinect multi-camera extrinsic calibration
Single and multiple view camera calibration tool
Markerless volumetric alignment for depth sensors. Contains the code of the work "Deep Soft Procrustes for Markerless Volumetric Sensor Alignment" (IEEE VR 2020).
MATLAB code for LiDAR-Camera-GNSS/INS extrinsic calibration based on hand-eye calibration method.
1.相机模型标定(鱼眼相机,普通相机);2.立体相机标定与极线矫正;3.RGB(/灰度)相机与深度相机(tof)标定,数据融合;4.外参标定
A python tutorial to use ball calibrating multiview camera system.
Extrinsic Calibration of Monocular Camera and Lidar using Planar Point To Plane Constraint
Extrinsics Calibration Real Sense Depth Camera Array
Implementation of TSAI camera calibration to compute intrinsic and extrinsic of the camera, with visualization of extrinsics.
This package introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Both the simulation and the experimental results confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed 30 meters away!
MATLAB and Python tools to extract and synchronize pointclouds and images from a rosbag for extrinsic calibration.
Extrinsic calibration for single point rangefinder and monocular vision systems
This repository contains sensor fusion between a lidar and camera, semantic segmentation on point clouds and ICP registration of multiple point clouds.
Camera calibration from scratch
Examples of camera calibration, camera parameters visualization
Lidar and camera calibration using LRANSAC. (Refactoring using C++)
This package introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Both the simulation and the experimental results confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed 30 meters away!
Implementation of Camera Calibration using Zhang’s Method.
Clone of Open PTrack (v2) adapted to ROS noetic. Note: This repository only contains the nodes related to the extrinsic calibration of a RGBD sensors' network (four, to be specific). All other nodes have been removed.