Target-Free and User-Friendly Online Extrinsic Calibration of LiDAR-IMU-Camera Systems Guided by Motion Excitation Assessment

Robust and reliable calibration forms the foundation of efficient multi-sensor fusion. Most existing calibration methods are offline and rely on artificial targets, which is time consuming and unfriendly to non-expert users. To improve efficiency, robustness and user-friendliness, this paper proposes a novel target-free LiDAR-IMU-camera online extrinsic calibration framework. An IMU-centric motion excitation assessment method is newly designed to assist non-expert users in data collection, serving as the termination criterion for extrinsic rotation initialization. We calibrate the extrinsic parameters of the LiDAR and camera relative to the IMU in real-time and in parallel without any priori knowledge: (i) For camera-IMU calibration, a new convergence criterion based on the coefficient of variation is proposed to judge if the calibration has been accomplished. (ii) For LiDAR-IMU calibration, a data selection strategy is designed to concurrently record a segment of original data from both the LiDAR and IMU during initialization. Subsequently, the selected data is utilized to refine the extrinsic parameters under a continuous-time batch optimization framework. Moreover, a scene recognition algorithm is proposed to autonomously select parameters according to indoor or outdoor features. Finally, we leverage the IMU as a bridge for computing the LiDAR-camera extrinsic parameters. Extensive real experiments on our self-recorded and public datasets show the high accuracy and robustness of the proposed method, with an average error of less than 0.01m in translation and
$0.5^{\circ }$
in rotation. (Supplementary video link:
https://youtu.be/Iph0kd_LIlk
)