Abstract

There are many applications of the combination of IMU (Inertial Measurements Unit) and camera in fields of electronic image stabilization, enhancement reality and navigation where camera-IMU relative pose calibration is one of the key technologies, which may effectively avoid the cases of insufficient feature points, unclear texture, blurred image, etc. In this paper, a new camera-IMU relative pose calibration method is proposed by establishing a BP neural network model. Thus we can obtain the transform from IMU inertial measurements to images and achieve camera-IMU relative pose calibration. The advantage of our method is the application of BP neural network using Levenberg-Marquardt algorithm, avoiding more complex calculations for the whole process. And it is convient for the application of camera-IMU combination system. Meanwhile, nonlinearities and noises are compensated while training and the impact of gravity can be ignored. Our experimental results demonstrated that this method can achieve camera-IMU relative pose calibration and the accuracy of quaternion estimation has reached about 0.01.

This work is licensed under a Creative Commons Attribution 4.0 License.