Awesome-LiDAR-Camera-Calibration
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A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes.
Outline
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Introduction
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Target-based methods
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Targetless methods
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Motion-based methods
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Scene-based-methods
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Traditional Methods
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Deep-learning Methods
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Other Toolboxes
0. Introduction
For applications such as autonomous driving, robotics, navigation systems, and 3-D scene reconstruction, data of the same scene is often captured using both lidar and camera sensors. To accurately interpret the objects in a scene, it is necessary to fuse the lidar and the camera outputs together. Lidar camera calibration estimates a rigid transformation matrix (extrinsics, rotation+translation, 6 DoF) that establishes the correspondences between the points in the 3-D lidar plane and the pixels in the image plane.
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1. Target-based methods
Paper |
Target |
Feature |
Optimization |
Toolbox |
Note |
Extrinsic Calibration of a Camera and Laser Range Finder (improves camera calibration), 2004 |
checkerboard |
C:Plane (a), L: pts in plane (m) |
point-to-plane |
CamLaserCalibraTool |
CN |
Fast Extrinsic Calibration of a Laser Rangefinder to a Camera, 2005 |
checkerboard |
C: Plane (a), L: Plane (m) |
plane(n/d) correspondence, point-to-plane |
LCCT |
* |
Extrinsic calibration of a 3D laser scanner and an omnidirectional camera, 2010 |
checkerboard |
C: plane (a), L: pts in plane (m) |
point-to-plane |
cam_lidar_calib |
* |
LiDAR-Camera Calibration using 3D-3D Point correspondences, 2017 |
cardboard + ArUco |
C: 3D corners (a), L: 3D corners (m) |
ICP |
lidar_camera_calibration |
* |
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard, 2017 |
checkerboard |
C: 2D corners (a), L: 3D corners (a) |
PnP, angle difference |
ILCC |
* |
Extrinsic Calibration of Lidar and Camera with Polygon, 2018 |
regular cardboard |
C: 2D edge, corners (a), L: 3D edge, pts in plane (a) |
point-to-line, point-inside-polygon |
ram-lab/plycal |
* |
Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences, 2018 |
checkerboard |
C: 3D edge, plane(a), L: 3D edge, pts in plane (a) |
direcion/normal, point-to-line, point-to-plane |
Matlab LiDAR Toolbox |
* |
Improvements to Target-Based 3D LiDAR to Camera Calibration, 2020 |
cardboard with ArUco |
C: 2d corners (a), L: 3D corners (a) |
PnP, IOU |
github |
* |
ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems, 2020 |
checkerboard |
C: 2D corners (a), L: 3D corners (a) |
PnP |
ACSC |
* |
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups, 2021 |
cardboard with circle & Aruco |
C: 3D points (a), L: 3D points (a) |
ICP |
velo2cam_ calibration |
* |
Single-Shot is Enough: Panoramic Infrastructure Based Calibration of Multiple Cameras and 3D LiDARs,2021 IROS |
panoramic infrastructure |
C: CCTag(a), L: corner points and vectors(a) |
PnP, ICP |
multiple-cameras-and-3D-LiDARs-extrinsic-calibration |
* |
C: camera, L: LiDAR, a: automaic, m: manual
2. Targetless methods
2.1. Motion-based methods
2.2. Scene-based methods
2.2.1. Traditional methods
Paper |
Feature |
Optimization |
Toolbox |
Note |
Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information, 2012 |
C:grayscale, L: reflectivity |
mutual information, BB steepest gradient ascent |
Extrinsic Calib |
* |
Automatic Calibration of Lidar and Camera Images using Normalized Mutual Information, 2013 |
C:grayscale, L: reflectivity, noraml |
normalized MI, particle swarm |
* |
* |
Automatic Online Calibration of Cameras and Lasers, 2013 |
C: Canny edge, L: depth-discontinuous edge |
correlation, grid search |
* |
* |
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera, 2020 |
semantic centroid |
PnP |
* |
* |
A Low-cost and Accurate Lidar-assisted Visual SLAM System, 2021 |
C: edge(grayscale), L: edge (reflectivity, depth projection) |
ICP, coordinate descent algorithms |
CamVox |
* |
Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments,2021 |
C:Canny edge(grayscale), L: depth-continuous edge |
point-to-line, Gaussian-Newton |
livox_camera_calib |
* |
CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes, 2021 |
C:straight line, L: straight line |
perspective3-lines (P3L) |
* |
CN |
Fast and Accurate Extrinsic Calibration for Multiple LiDARs and Cameras,2021 |
C:Canny edge(grayscale), L: depth-continuous edge |
point-to-plane, point-to-edge |
mlcc |
* |
2.2.2. Deep-learning methods
3. Other toolboxes