[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-82638":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":15,"stars7d":13,"stars30d":16,"stars90d":15,"forks30d":15,"starsTrendScore":17,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":20,"hasPages":20,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":23,"readmeContent":24,"aiSummary":25,"trendingCount":15,"starSnapshotCount":15,"syncStatus":26,"lastSyncTime":27,"discoverSource":28},82638,"FAST-Calib2","xuankuzcr\u002FFAST-Calib2","xuankuzcr","A Robust LiDAR-camera Calibration Tool for Large-spot LiDARs.","",null,"C++",131,15,108,0,23,7,53.41,"GNU General Public License v2.0",false,"main",[],"2026-06-12 04:01:38","# FAST-Calib2\n\n## LiDAR-Camera Extrinsic Calibration with Reflective Annular Targets\n\nFAST-Calib2 extends [FAST-Calib](https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FFAST-Calib) to LiDAR-camera modules that were previously hard to calibrate due to **low-quality point clouds**. With a custom-designed reflective annular calibration target, it enables robust center extraction on **large-spot solid-state and mechanical LiDARs**, including Mid360, Avia, Ouster, XT32, JT128, Airy, E1R, and Adaps Photonics Spad LiDAR.\n\n**Key highlights include:**\n\n1. A self-designed 3D reflective annular calibration target that avoids center extraction errors caused by hole-edge inflation and bleeding artifacts in previous circular-hole calibration boards.\n2. A robust concentric-circle fitting method that uses the fixed inner and outer annulus radii as geometric constraints.\n3. Automatic calibration board ROI extraction without manual pass-through tuning.\n4. Geometry and radius quality checks for extracted annulus centers.\n5. Single-scene and multi-scene LiDAR-camera extrinsic calibration without initial extrinsic parameters.\n\n📬 For further assistance or inquiries, please feel free to contact Chunran Zheng at zhengcr@connect.hku.hk.\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\".\u002Fpics\u002Fcover.jpg\" width=\"100%\">\n  \u003Cfont color=#a0a0a0 size=2>Mid360 calibration example.\u003C\u002Ffont>\n\u003C\u002Fp>\n\n## 1. Prerequisites\n\nPCL>=1.8, OpenCV>=4.0.\n\n## 2. Calibration Target\n\nFAST-Calib2 uses four reflective annuli and four visual markers on one board. The annuli are used by LiDAR center extraction, while the visual markers are used by the camera pipeline.\n\nMaterials:\n\n- Board: PVC\n- Reflective annulus stickers: 3M engineering-grade reflective film\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\".\u002Fpics\u002FFAST-Calib2-board.png\" width=\"100%\">\n  \u003Cfont color=#a0a0a0 size=2>Reflective annular calibration target and annotated dimensions.\u003C\u002Ffont>\n\u003C\u002Fp>\n\nDIY Calibration Target Tips:\n\n1. Fabricate the board based on the schematic. Ensure a minimum thickness of 1 cm to avoid bending.\n2. Apply reflective annulus stickers to the designated ring positions on the fabricated board.\n\n## 3. Method Overview\n\nBoth LiDAR pipelines first **locate the calibration board automatically**, fit the board plane, and align the plane to `Z=0`. Center extraction is then performed in the aligned board frame.\n\nSolid-state LiDAR pipeline:\n\n1. Extract high-reflectivity annulus points on the fitted board plane.\n2. Cluster the extracted annulus points.\n3. Fit robust single circles as the default center estimate.\n4. Optionally extract annulus boundary points and fit fixed inner\u002Fouter radius concentric circles.\n5. Select the best result by checking four-center geometry consistency against the known target geometry.\n\nMechanical LiDAR pipeline:\n\n1. Use LiDAR `ring` order to find intensity transition points on the annulus boundary.\n2. Try both interpolated boundary points and high-reflectivity-side boundary points.\n3. Cluster the extracted boundary points.\n4. Fit fixed inner\u002Fouter radius concentric circles.\n5. Select the best result by checking four-center geometry consistency against the known target geometry.\n\nThe final quality checks include center-to-center geometry error and annulus radius consistency.\n\n## 4. Run Examples\n\nPrepare static acquisition data in the `calib_data` folder (Download the example data from [Google Drive](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1VnMCsGj3Gat7dxe6IION0SfS7jYNMw1g?usp=sharing)):\n\n- rosbag containing point cloud messages\n- corresponding image\n\nRun single-scene calibration:\n\n```bash\nroslaunch fast_calib calib.launch\n```\n\nAfter collecting at least three scenes, run multi-scene joint calibration:\n\n```bash\nroslaunch fast_calib multi_calib.launch\n```\n\nTypical multi-scene target placement:\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\".\u002Fpics\u002Fmulti-scene.jpg\" width=\"100%\">\n  \u003Cfont color=#a0a0a0 size=2>Placement of the calibration target for multi-scene data collection: (a) facing forward, (b) oriented to the right, (c) oriented to the left.\u003C\u002Ffont>\n\u003C\u002Fp>\n\n## 5. Standalone LiDAR Center Extraction Test\n\n\u003Cdetails>\n\u003Csummary>Show Unit Test Usage\u003C\u002Fsummary>\n\nThe repository also provides a LiDAR-only test tool for checking annulus center extraction before running full camera-LiDAR calibration.\n\nLoad parameters:\n\n```bash\nrosparam load config\u002Fqr_params.yaml \u002F\nrosparam set \u002Foutput_path \u002Fhome\u002Fchunran\u002F02_calib_ws\u002Fsrc\u002FFAST-Calib\u002Foutput\n```\n\nRun solid-state LiDAR data:\n\n```bash\nrosrun fast_calib lidar_center_test calib_data\u002Ffast-calib2-data\u002Fleft.bag \u002Flivox\u002Flidar solid\nrosrun fast_calib lidar_center_test calib_data\u002Ffast-calib2-data\u002Fmid.bag \u002Flivox\u002Flidar solid\nrosrun fast_calib lidar_center_test calib_data\u002Ffast-calib2-data\u002Fright.bag \u002Flivox\u002Flidar solid\n```\n\nRun mechanical LiDAR data:\n\n```bash\nrosrun fast_calib lidar_center_test calib_data\u002Fhesai-jt128\u002Fleft.bag \u002Flidar_points mech\nrosrun fast_calib lidar_center_test calib_data\u002Fhesai-jt128\u002Fmid.bag \u002Flidar_points mech\nrosrun fast_calib lidar_center_test calib_data\u002Fhesai-jt128\u002Fright.bag \u002Flidar_points mech\n```\n\nThe test tool writes:\n\n- `*_centers.txt`: extracted annulus center coordinates\n- `*_debug_cloud.pcd`: board point cloud, annulus points, boundary points, and center markers for visualization\n\nDebug PCD colors:\n\n- Board points: intensity color map\n- Annulus points: green\n- Solid-LiDAR boundary points: red\n- Centers: white spheres\n\n\u003C\u002Fdetails>\n","FAST-Calib2 是一款专为大光斑激光雷达设计的鲁棒性激光雷达-相机外参标定工具。该项目通过自定义设计的反射环形标定板，解决了由于点云质量低导致难以标定的问题，适用于多种类型的固态和机械激光雷达。其核心功能包括使用几何约束的同心圆拟合方法、自动提取标定板感兴趣区域（ROI）以及无需初始外参参数的单场景或多场景标定。此外，项目还提供了详细的标定目标制作指南，并要求PCL和OpenCV库的支持。该工具非常适合需要高精度激光雷达与相机联合标定的应用场景，如自动驾驶、机器人导航等领域。",2,"2026-06-11 04:08:48","CREATED_QUERY"]