Louis Wiesmann;Thomas Läbe;Lucas Nunes;Jens Behley;Cyrill Stachniss
{"title":"利用校准环境对感知系统进行内在和外在联合校准","authors":"Louis Wiesmann;Thomas Läbe;Lucas Nunes;Jens Behley;Cyrill Stachniss","doi":"10.1109/LRA.2024.3457385","DOIUrl":null,"url":null,"abstract":"Basically all multi-sensor systems must calibrate their sensors to exploit their full potential for state estimation such as mapping and localization. In this letter, we investigate the problem of extrinsic and intrinsic calibration of perception systems. Traditionally, targets in the form of checkerboards or uniquely identifiable tags are used to calibrate those systems. We propose to use a whole calibration environment as a target that supports the intrinsic and extrinsic calibration of different types of sensors. By doing so, we are able to calibrate multiple perception systems with different configurations, sensor types, and sensor modalities. Our approach does not rely on overlaps between sensors which is often otherwise required when using classical targets. The main idea is to relate the measurements for each sensor to a precise model of the calibration environment. For this, we can choose for each sensor a specific method that best suits its calibration. Then, we estimate all intrinsics and extrinsics jointly using least squares adjustment. For the final evaluation of a LiDAR-to-camera calibration of our system, we propose an evaluation method that is independent of the calibration. This allows for quantitative evaluation between different calibration methods. The experiments show that our proposed method is able to provide reliable calibration.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment\",\"authors\":\"Louis Wiesmann;Thomas Läbe;Lucas Nunes;Jens Behley;Cyrill Stachniss\",\"doi\":\"10.1109/LRA.2024.3457385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Basically all multi-sensor systems must calibrate their sensors to exploit their full potential for state estimation such as mapping and localization. In this letter, we investigate the problem of extrinsic and intrinsic calibration of perception systems. Traditionally, targets in the form of checkerboards or uniquely identifiable tags are used to calibrate those systems. We propose to use a whole calibration environment as a target that supports the intrinsic and extrinsic calibration of different types of sensors. By doing so, we are able to calibrate multiple perception systems with different configurations, sensor types, and sensor modalities. Our approach does not rely on overlaps between sensors which is often otherwise required when using classical targets. The main idea is to relate the measurements for each sensor to a precise model of the calibration environment. For this, we can choose for each sensor a specific method that best suits its calibration. Then, we estimate all intrinsics and extrinsics jointly using least squares adjustment. For the final evaluation of a LiDAR-to-camera calibration of our system, we propose an evaluation method that is independent of the calibration. This allows for quantitative evaluation between different calibration methods. The experiments show that our proposed method is able to provide reliable calibration.\",\"PeriodicalId\":13241,\"journal\":{\"name\":\"IEEE Robotics and Automation Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Robotics and Automation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10670288/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10670288/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment
Basically all multi-sensor systems must calibrate their sensors to exploit their full potential for state estimation such as mapping and localization. In this letter, we investigate the problem of extrinsic and intrinsic calibration of perception systems. Traditionally, targets in the form of checkerboards or uniquely identifiable tags are used to calibrate those systems. We propose to use a whole calibration environment as a target that supports the intrinsic and extrinsic calibration of different types of sensors. By doing so, we are able to calibrate multiple perception systems with different configurations, sensor types, and sensor modalities. Our approach does not rely on overlaps between sensors which is often otherwise required when using classical targets. The main idea is to relate the measurements for each sensor to a precise model of the calibration environment. For this, we can choose for each sensor a specific method that best suits its calibration. Then, we estimate all intrinsics and extrinsics jointly using least squares adjustment. For the final evaluation of a LiDAR-to-camera calibration of our system, we propose an evaluation method that is independent of the calibration. This allows for quantitative evaluation between different calibration methods. The experiments show that our proposed method is able to provide reliable calibration.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.