校准
计算机科学
人工智能
利用
机器人学
计算机视觉
忠诚
机器人
遥感
地理
数学
计算机安全
电信
统计
作者
Xiangyang Zhi,Jiawei Hou,Yiren Lu,Laurent Kneip,Soren Schwertfeger
标识
DOI:10.1109/iros47612.2022.9982031
摘要
Spatiotemporal calibration of sensors, especially of those which do not share their fields of view, is becoming increasingly important in the fields of autonomous driving and robotics. This paper presents a general sensor calibration method, named Multical, that makes use of multiple planar calibration targets whose poses will be estimated alongside spatiotemporal calibration. Multical exploits continuous-time curves to represent the state of the sensor platform during data collection, and thus is a general framework to calibrate different kinds of sensors and deal with both spatial as well as temporal offsets. Multical includes algorithms to estimate the initial guesses of spatial transformations between sensors, and also the relative poses between calibration targets. Users do not need to provide any extrinsic priors. We apply the proposed calibration approach to both simulated and real-world experiments, and the results demonstrate the high fidelity of the proposed method.
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