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Autonomous driving has spurred the development of sensor fusion techniques,which combine data from multiple sensors to improve system performance. Inparticular, localization system based on sensor fusion , such as VisualSimultaneous Localization and Mapping (VSLAM), is an important component inenvironment perception, and is the basis of decision-making and motion controlfor intelligent vehicles. The accuracy of extrinsic calibration parametersbetween camera and IMU has significant effect on the positioning precision whenperforming VSLAM system. Currently, existing methods are time-consuming usingcomplex optimization methods and sensitive to noise and outliers due tooff-calibration, which can negatively impact system performance. To addressthese problems, this paper presents a fast and robust camera-IMU onlinecalibration method based space coordinate transformation constraints and SVD(singular Value Decomposition) tricks. First, constraint equations areconstructed based on equality of rotation and transformation matrices betweencamera frames and IMU coordinates at different moments. Secondly, the externalparameters of the camera-IMU are solved using quaternion transformation and SVDtechniques. Finally, the proposed method is validated using ROS platform, whereimages from the camera and velocity, acceleration, and angular velocity datafrom the IMU are recorded in a ROS bag file. The results showed that theproposed method can achieve robust and reliable camera-IMU online calibrationparameters results with less tune consuming and less uncertainty.

A Fast and Robust Camera-IMU Online Calibration Method For Localization System