{"title":"不确定条件下相机和激光雷达内、外统一标定","authors":"Julius Kummerle, Tilman Kuhner","doi":"10.1109/ICRA40945.2020.9197496","DOIUrl":null,"url":null,"abstract":"Many approaches for camera and LiDAR calibration are presented in literature but none of them estimates all intrinsic and extrinsic parameters simultaneously and therefore optimally in a probabilistic sense.In this work, we present a method to simultaneously estimate intrinsic and extrinsic parameters of cameras and LiDARs in a unified problem. We derive a probabilistic formulation that enables flawless integration of different measurement types without hand-tuned weights. An arbitrary number of cameras and LiDARs can be calibrated simultaneously. Measurements are not required to be time-synchronized. The method is designed to work with any camera model.In evaluation, we show that additional LiDAR measurements significantly improve intrinsic camera calibration. Further, we show on real data that our method achieves state-of-the-art calibration precision with high reliability.","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"40 1","pages":"6028-6034"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Unified Intrinsic and Extrinsic Camera and LiDAR Calibration under Uncertainties\",\"authors\":\"Julius Kummerle, Tilman Kuhner\",\"doi\":\"10.1109/ICRA40945.2020.9197496\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many approaches for camera and LiDAR calibration are presented in literature but none of them estimates all intrinsic and extrinsic parameters simultaneously and therefore optimally in a probabilistic sense.In this work, we present a method to simultaneously estimate intrinsic and extrinsic parameters of cameras and LiDARs in a unified problem. We derive a probabilistic formulation that enables flawless integration of different measurement types without hand-tuned weights. An arbitrary number of cameras and LiDARs can be calibrated simultaneously. Measurements are not required to be time-synchronized. The method is designed to work with any camera model.In evaluation, we show that additional LiDAR measurements significantly improve intrinsic camera calibration. Further, we show on real data that our method achieves state-of-the-art calibration precision with high reliability.\",\"PeriodicalId\":73286,\"journal\":{\"name\":\"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation\",\"volume\":\"40 1\",\"pages\":\"6028-6034\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRA40945.2020.9197496\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRA40945.2020.9197496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unified Intrinsic and Extrinsic Camera and LiDAR Calibration under Uncertainties
Many approaches for camera and LiDAR calibration are presented in literature but none of them estimates all intrinsic and extrinsic parameters simultaneously and therefore optimally in a probabilistic sense.In this work, we present a method to simultaneously estimate intrinsic and extrinsic parameters of cameras and LiDARs in a unified problem. We derive a probabilistic formulation that enables flawless integration of different measurement types without hand-tuned weights. An arbitrary number of cameras and LiDARs can be calibrated simultaneously. Measurements are not required to be time-synchronized. The method is designed to work with any camera model.In evaluation, we show that additional LiDAR measurements significantly improve intrinsic camera calibration. Further, we show on real data that our method achieves state-of-the-art calibration precision with high reliability.