Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants

Analyzing sensor data of plants and monitoring plant performance is a central element in different agricultural robotics applications. In plant science, phenotyping refers to analyzing plant traits for monitoring growth, for describing plant properties, or characterizing the plant's overall performance. It plays a critical role in the agricultural tasks and in plant breeding. Recently, there is a rising interest in using 3D data obtained from laser scanners and 3D cameras to develop automated non-intrusive techniques for estimating plant traits. In this paper, we address the problem of registering 3D point clouds of the plants over time, which is a backbone of applications interested in tracking spatio-temporal traits of individual plants. Registering plants over time is challenging due to its changing topology, anisotropic growth, and non-rigid motion in between scans. We propose a novel approach that exploits the skeletal structure of the plant and determines correspondences over time and drives the registration process. Our approach explicitly accounts for the non-rigidity and the growth of the plant over time in the registration. We tested our approach on a challenging dataset acquired over the course of two weeks and successfully registered the 3D plant point clouds recorded with a laser scanner forming a basis for developing systems for automated temporal plant-trait analysis.