Proposal of a Point Cloud Inter-Day Registration Method for Agricultural UAV Monitoring

Continuous crop monitoring is essential for in- spection of pests, diseases, and the evaluation of crop growth. It requires registering sensor data from the same field over a period of time. Agricultural fields can be effectively monitored by cameras or LiDAR mounted on unmanned aerial vehicles (UAVs). However, due to significant changes in crop growth and soil conditions over different measurement periods, con- ventional data registration methods often fail to reduce the loss of accuracy. In this study, we proposed a point cloud registration method that utilizes static geometric information of crop rows and terrain to align maps from different measurement periods. In our experiments, we first created a map with accurate positional data using LiDAR at the beginning of the season. We then generated maps by Structure from Motion (SfM) 5 days later, when ground information had notably changed due to tractor activity, and the other map 19 days later when crops had grown considerably. The proposed method was applied to register the maps generated in different periods. The result of a demonstration by using precise positional information obtained at the start of the season showed that we were able to align maps taken up to 19 days apart with displacements of no more than 30 cm.