A novel motion-based online temporal calibration method for multi-rate sensors fusion

• Published in: Information fusion Vol. 88; pp. 59 - 77
• Main Authors: Liu, Wanli, Li, Zhixiong, Sun, Shuaishuai, Du, Haiping, Sotelo, Miguel Angel
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022
• Subjects: Motion constraints; Multi-rate sensors fusion; Temporal offset; Temporal self-calibration; Temporal self-calibration; Motion constraints; Temporal offset; Multi-rate sensors fusion
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2022.07.004

•A novel motion-based online temporal calibration method is developed.•The calibration method adopts multi-state constraint Kalman filter for real-time calculation.•Multi-rate sensors calibration has been achieved in an online manner.•The advantages of the new method over existing ones include targetless, online, and high-precision temporal calibration. Accurate and robust calibration is an essential prerequisite for multi-rate sensors fusion. However, most existing calibration methods ignore the temporal calibration and assumed the timestamps of the multi-rate sensors are precisely aligned; more importantly, many approaches are designed for offline calibration. For these reasons, this paper develops a novel online temporal calibration method for multi-rate sensors fusion based on the motion constrains of the sensors. In this new calibration framework, the high update rate inertial measurement unit (IMU) is utilized as the unified calibrating references, while other moderate or low-frequency target sensors can be estimated based on the reference IMU. As a result, the targetless, online, and high-precision temporal self-calibration can be achieved. During the calibration, an improved multi-state constraint Kalman filter (I-MSCKF) algorithm is proposed for both position and temporal states estimation of the multi-rate sensors to establish a multi-constraint filter and correct the temporal offset error in a real-time manner. Furthermore, the motion constraints models in the two-dimensional (2D) planar and three-dimensional (3D) space are developed from per-sensor ego-motion to enhance the robust and reliable abilities of the proposed temporal self-calibration method. Experimental results demonstrate that the proposed method can accurately and online estimate the temporal offset error and transformation parameters, which significantly improves the performance of moving trajectory estimation for robots equipped with the multi-rate sensors.