3D log recognition and pose estimation for robotic forestry machine

Successful recognition and pose estimation of logs and trees as well as workspace modeling in the forest environment is essential for extensive automation of the harvesting and logging tasks of forestry machines. However, the free form features of logs, few reliable textural features, large edge extraction errors, and segmentation faults caused by the barks on the surface of the logs present clear challenges for recognition and classification. To solve these problems, robust algorithms able to recognize and estimate poses of a variety of objects even under poor and partial inputs need to be developed. In this paper we focus on the most relevant task of recognizing and estimating postures of a bunch of logs located on the ground with varying orientation and distance. Experiments carried out with the help of a structured light camera demonstrate the feasibility of the proposed algorithm.