Uncontrolled Two-Step Iterative Calibration Algorithm for Lidar-IMU System

Calibration of sensors is critical for the precise functioning of lidar-IMU systems. However, the accuracy of the system can be compromised if motion distortion is not considered. This study proposes a novel uncontrolled two-step iterative calibration algorithm that eliminates motion distortion and improves the accuracy of lidar-IMU systems. Initially, the algorithm corrects the distortion of rotational motion by matching the original inter-frame point cloud. Then, the point cloud is further matched with IMU after the prediction of attitude. The algorithm performs iterative motion distortion correction and rotation matrix calculation to obtain high-precision calibration results. In comparison with existing algorithms, the proposed algorithm boasts high accuracy, robustness, and efficiency. This high-precision calibration result can benefit a wide range of acquisition platforms, including handheld, unmanned ground vehicle (UGV), and backpack lidar-IMU systems.

Automated summary

This study proposes a novel uncontrolled two-step iterative calibration algorithm that eliminates motion distortion and improves the accuracy of lidar-IMU systems. The algorithm corrects the distortion of rotational motion by matching the original inter-frame point cloud and then matches the point cloud with IMU after attitude prediction. The algorithm performs iterative motion distortion correction and rotation matrix calculation to obtain high-precision calibration results, demonstrating high accuracy, robustness, and efficiency compared to existing algorithms.