Online Initialization and Automatic Camera-IMU Extrinsic Calibration for Monocular Visual-Inertial SLAM

Most of the existing monocular visual-inertial SLAM techniques assume that the camera-IMU extrinsic parameters are known, therefore these methods merely estimate the initial values of velocity, visual scale, gravity, biases of gyroscope and accelerometer in the initialization stage. However, it's usually a professional work to carefully calibrate the extrinsic parameters, and it is required to repeat this work once the mechanical configuration of the sensor suite changes slightly. To tackle this problem, we propose an online initialization method to automatically estimate the initial values and the extrinsic parameters without knowing the mechanical configuration. The biases of gyroscope and accelerometer are considered in our method, and a convergence criteria for both orientation and translation calibration is introduced to identify the convergence and to terminate the initialization procedure. In the three processes of our method, an iterative strategy is firstly introduced to iteratively estimate the gyroscope bias and the extrinsic orientation. Secondly, the scale factor, gravity, and extrinsic translation are approximately estimated without considering the accelerometer bias. Finally, these values are further optimized by a refinement algorithm in which the accelerometer bias and the gravitational magnitude are taken into account. Extensive experimental results show that our method achieves competitive accuracy compared with the state-of-the-art with less calculation.