LiDAR Point Cloud Correction and Location Based on Multisensor Fusion

It is difficult to solve motion distortion and poor positioning accuracy caused by point cloud distortion and error accumulation in a LiDAR moving scene using a single sensor. To address this problem, a LiDAR point cloud distortion correction and positioning method that combines inertial measurement unit data and wheel tachometer data is proposed. First, the data of the inertial measurement unit and the wheel tachometer are preprocessed by an integration method based on the time of the LiDAR data. Next, the fusion data and the LiDAR point cloud data are fused to correct the position and pose of the laser point cloud distorted by motion. Finally, the linear interpolation method is used to ensure the time synchronization and availability of data between sensors and ultimately improve the positioning accuracy of the odometer; the calculated pose was used as the optimal initial value of the odometer iteration. The experimental results show that compared with the traditional method that does not use multisensor fusion (LOAM and F-LOAM), the proposed method's root mean square error of positioning on the open data set experiment is reduced by 81.11% and 21.54%, respectively, the root mean square error of positioning of the proposed method on the self-testing data concentration period is reduced by 52.76% and 24.29%, respectively.