  Autonomous driving has spurred the development of sensor fusion techniques,
which combine data from multiple sensors to improve system performance. In
particular, localization system based on sensor fusion , such as Visual
Simultaneous Localization and Mapping (VSLAM), is an important component in
environment perception, and is the basis of decision-making and motion control
for intelligent vehicles. The accuracy of extrinsic calibration parameters
between camera and IMU has significant effect on the positioning precision when
performing VSLAM system. Currently, existing methods are time-consuming using
complex optimization methods and sensitive to noise and outliers due to
off-calibration, which can negatively impact system performance. To address
these problems, this paper presents a fast and robust camera-IMU online
calibration method based space coordinate transformation constraints and SVD
(singular Value Decomposition) tricks. First, constraint equations are
constructed based on equality of rotation and transformation matrices between
camera frames and IMU coordinates at different moments. Secondly, the external
parameters of the camera-IMU are solved using quaternion transformation and SVD
techniques. Finally, the proposed method is validated using ROS platform, where
images from the camera and velocity, acceleration, and angular velocity data
from the IMU are recorded in a ROS bag file. The results showed that the
proposed method can achieve robust and reliable camera-IMU online calibration
parameters results with less tune consuming and less uncertainty.
