Camera-IMU Calibration Through Improved Trajectory Estimation

Camera-IMU calibration is essential for achieving precise and dependable data fusion in many augmented reality applications. However, most conventional methods rely on trajectory optimization based on image features, which can be easily affected by other factors. To address this issue, we propose a new calibration framework that utilizes a high-precision external device, such as a robotic arm, to optimize the trajectory of the camera for camera-IMU calibration. The proposed method has two main steps: 1) Optimize camera trajectory estimation using the external device; 2) Calculate camera-IMU parameters with the optimized camera trajectory and pre-processed IMU data. The main advantages of our proposed method are: 1) Utilizing a high-precision external device to reduce image detection errors for improving camera trajectory accuracy; 2) Reducing sensitivity to unexpected factors and increasing the robustness of camera-IMU calibration in various environments. Extensive experiments show that the proposed camera-IMU calibration framework can significantly improve the accuracy and robustness of calibration results by about 35%.