针对单一传感器难以解决激光雷达在运动场景中因为点云畸变和误差累积产生的运动失真与定位精度差的问题，提出一种融合惯性测量单元数据和轮速计数据的激光雷达点云畸变矫正与定位方法。首先，以激光雷达数据为时刻基准，利用积分的方法对惯性测量单元和轮速计的数据进行预处理；之后，将融合数据与激光雷达数据融合，以矫正产生畸变的激光点云；最后，利用线性插值的方式来保证传感器间数据的时间同步，并将计算的位姿作为里程计迭代计算的初值，降低计算复杂度并提高里程计的定位精度。实验结果表明，相比没有采用多传感器融合的传统方案（LOAM、F-LOAM），在公开数据集实验中，所提方法的定位均方根误差分别降低了81.11%和21.54%，在自测数据集实验中，定位均方根误差分别降低了52.76%和24.29%。

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.