A dynamic and static combined camera-IMU extrinsic calibration method based on continuous-time trajectory estimation

The accuracy of extrinsic calibration between the camera and IMU is crucial for precise localization and mapping in visual-inertial navigation systems. However, traditional camera-IMU extrinsic calibration methods generally overlook the respective characteristics of the camera and inertial sensor, usually calibrating the entire platform as a whole. Accordingly, while rapid motion can adequately activate the IMU, the image blurring and loss of the visual field caused by excessive motion would affect the accuracy of calibration to some extent. To address this problem, we propose a dynamic and static combined camera-IMU extrinsic calibration method based on continuous-time trajectory estimation. The proposed method introduces a motion capture system as a bridge and skillfully decouples the calibration system into two subsystems, dynamic and static. Therefore, the restrictions between the camera and IMU could be circumvented, with activating IMU sufficiently and avoiding the motion blurring of images, simultaneously. A series of simulations and real-world experiments were conducted to evaluate the effectiveness and accuracy of the proposed calibration method. Moreover, the result was compared with other camera-IMU calibration methods and applied in real-world SLAM to verify the reliability. All the results demonstrate that the proposed method could achieve a more reliable extrinsic with higher precision and consistency. •A novel dynamic and static combined camera-IMU extrinsic calibration method is proposed.•The motion capture system is introduced as a bridge to decouple the system.•We achieve a trade-off between IMU activation and maintaining the camera’s field of view.•Experiments demonstrate the effectiveness and consistency of the proposed method.•Experiments show lower APE error of SLAM compared with SOTA approaches.