In response to the problems as multiple interference factors, and long computation time in processing field information using current radar perception, an automatic alignment system that combined laser radar and Beidou positioning for tobacco harvesters. The control algorithm fit the point cloud processing centerline based on the historical positioning coordinates of the aircraft body, effectively solving problems such as deviate guide lines and misalignment caused by aircraft body deviation. Based on the collected point cloud information of tobacco plants, the current row spacing and plant height of the field were calculated as the decision basis for extracting the region of interest (ROI). To shorten the processing time of point clouds, the length, width, and height range of ROI space were dynamically adjusted, and interference point clouds were filtered out. A crop row classification method was proposed based on point cloud density, and the initial clustering center of K-means algorithm was determined through vertical projection and sliding window method to improve the accuracy of tobacco row navigation line fitting. The optimal preview algorithm was used to track and drive the obtained forward tobacco navigation line. Automatic parallel testing was conducted in three tobacco fields with different row spacing. The maximum lateral error between the center of the machine and the actual centerline of the tobacco row was 0.107m, with an average lateral error of 0.074m. The accuracy of the harvest navigation line was 94.8%, indicating that the system can effectively ensure automatic parallel driving of the harvester. The proposed dynamic ROI point cloud processing algorithm reduced the average point cloud processing time from 536.2ms to 148.95ms compared with existing algorithms. The tobacco harvester automatic alignment system and dynamic ROI point cloud processing algorithm proposed can provide technical references for unmanned autonomous operation of field planting crop equipment.