Orchard Trunk Detection Algorithm for Agricultural Robot Based on Laser Radar

In the view of the influence of slopes and weeds in orchard on the detection accuracy of fruit trees in hilly areas, a tree trunk detection algorithm based on adaptive density clustering was proposed. Firstly, the single line LiDAR was used to obtain the environmental information. That was through data preprocessing, the noise points and the unusable data points were filtered out, the clustering radius was set with the trunk as the target, and the clustering threshold was set adaptively according to the distance from the data points to the LiDAR, and then the preliminary clustering was completed. As following, the features of the preliminary clustering results and the data points in the ground class that huge also roughly in a straight line were used. After this, the class which was over the certain number of data point were used in the second curve fitting. Also, the class that fitting radius was greater than a certain threshold value was regarded as ground interference and needed to be eliminated. Finally, the class which data points were more than a certain number of adjacent data points were regarded as weed branches and leaves and eliminated by using the feature of discontinuous distance between data points in weed branches and leaves, thus the detection of tree trunks or orchard was completed. The experimental results showed that with no interference, the false detection rate was 0.76%, the missed detection rate was 1.90%, and the average accuracy rate was 97.3%, respectively; the average accuracy rate of tree detection was 96.1% when there was only ground interference; the average accuracy rate of tree detection was 91.4% when there was only weed interference, and the average accuracy rate of tree detection was 91.9% when there was both ground and weed interference. The overall average accuracy from all situations was 95.5%. This method could be used to detect trunk in arborization orchard with obvious tree trunk in hilly area and provide environmental understanding for the navigation of precision agricultural equipment in the orchard in hilly area. © 2020, Chinese Society of Agricultural Machinery. All right reserved.