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Real-Time Temporal and Rotational Calibration of Heterogeneous Sensors Using Motion Correlation Analysis

惯性测量装置 计算机视觉 里程计 人工智能 稳健性(进化) 计算机科学 传感器融合 偏移量(计算机科学) 校准 机器人 数学 移动机器人 生物化学 化学 统计 程序设计语言 基因
作者
Kejie Qiu,Tong Qin,Jie Pan,Siqi Liu,Shaojie Shen
出处
期刊:IEEE Transactions on Robotics [Institute of Electrical and Electronics Engineers]
卷期号:37 (2): 587-602 被引量:28
标识
DOI:10.1109/tro.2020.3033698
摘要

Accurate and robust calibration is crucial to a multisensor fusion-based system. The calibration of heterogeneous sensors is particularly challenging because of the huge difference of the captured sensor data. On the other hand, many calibration approaches ignore temporal calibration that is in fact as important as spatial calibration. In this article, we focus on the temporal calibration of heterogeneous sensors, and the corresponding extrinsic rotation is also derived. Most existing methods are specialized for a certain sensor combination, such as an inertial measurement unit (IMU) camera or a camera-Lidar system. However, heterogeneous multisensor fusion is a tendency in the robotics area, so a unified calibration method is desired. To this end, we leverage the 3-D rotational motion feature for calibration, and auxiliary calibration boards are not needed since multiple odometry methods are available to capture 3-D sensor motion. Using a high-frequency IMU as the calibration reference, an IMU-centric scheme is designed to achieve a unified framework that adapts to various target sensors that can independently estimate 3-D rotational motion. By combining independent IMU-centric calibration pairs, an arbitrary pair of sensors can also be calibrated using the same reference IMU. Due to a novel 3-D motion correlation quantification and analysis mechanism, the temporal offset can be first estimated in real time. Given temporally aligned sensor motion, the extrinsic rotation can be derived in closed-form in the same 3-D motion correlation mechanism. Experimental results of certain sensor combinations show the accuracy and robustness of the proposed method through comparison with state-of-the-art calibration approaches, and the calibration result of a heterogeneous multisensor set demonstrates the scalability and versatility of our method.
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